Video link
I know the final few minutes of this were posted yesterday (and I appreciate the enthusiasm there!) but I figured I'd also go ahead and post the full run and dive into some explanations I thought were interesting. First though I'd like to thank some major strategic contributors from Twitch - this section will be a little lengthy but it does have some insights as well, so skip at your own risk if you're here for details.
Big Thanks to:
Kog was the first to notice the way to determine the right Atraks from her replications, which is by looking for small changes in the health bar. He was also the first to suggested a ranged stun for the third stun, which was a strategic breakthrough. These critical contributions made the run possible, but I also have Kog to thank for a huge number of minor optimizations. His investment in the challenge really improved my outlook on it, too, so huge thanks to Kog for his above and beyond help.
Fryco pointed me in the direction of mods that relieved a ton of pressure from adds and allowed me to focus near single-mindedly on the rest of the mechanics. Specifically, arc resist mods are just crazy good in this encounter and I didn't know that, but the enhanced operator augment mod is actually insane here too. With this setup I was nearly invincible, it took massive misplays for me to die to adds.
Wildfire corrected my understanding of the wipe mechanic's timing. I was under the impression that a strict timer started at the beginning of the encounter, but Wildfire pointed out that the timer actually starts after every stun. This helped correct a huge, needless amount of rushing on my end that made consistency possible in a way it would have taken forever to arrive at on my own.
GrimAgent suggested crouching to cleanse the replication from myself, which seemed to make the self-cleanse "hitbox" more forgiving. It's hard to overstate the importance of consistent cleanses, but runs depends entirely on it - a slow cleanse (we're talking ~an extra second in a lot of cases) means one less guess on the boss, which reduces chances of success by 25% on the spot. A "very slow" cleanse that's 3 seconds late is a wipe. It's a kind of wonky mechanic that needs to be done near perfectly, so a little extra forgiveness with it helped massively. Grim also was around for a lot of the pre-attempt theorycrafting to get runs off the ground. Also thanks to Grim for pretty much all graphical stuff that went into the video (thumbnail and RNG explanation image).
Butters also helped with quite a bit of testing during the early phases when I still needed a person to rely on for some help in the encounter while I was attempting various solo experiments, so thank you very much for that help Butters.
Faldo_ found the best cleanse spot, which was the doorway of the airlock itself. There aren't many places to check within the airlock and I had tried what I thought was everything with varying levels of success, but Faldo's approach turned out by-far best. Interestingly, the doorway is too short to cleanse on without crouching, so it seems like this development came in two small steps for a major improvement to consistency. Faldo is also the first player to achieve final stand in this solo which is a huge achievement.
One thing I found that I'm personally satisfied with is the "soccer" strategy of cleansing two replications at once after juggling them. For a long time I was cleansing replications in individual airlocks after juggling them (for speed), but after deciding that keeping more airlocks functional was a higher priority, the cleanse-one-kick-one approach was my idea. This whole mechanic came to my complete satisfaction because the replications sometimes bounce away immediately after stuns, which I complained about as "the wrong time to play soccer" every time it caused a wipe (which wasn't uncommon), so to have the kicking become part of the intended strategy was serendipitous to say the least.
Kyro and Lemons suggested I hold onto my super for movement reasons instead of using it to clear servitors. While I don't use that strategy in this run, it was an impactful change that allowed me to continue runs that would have otherwise been impossible.
Disco from my day 1 raid team was the first person I heard mention the idea of self-cleansing with ricochet rounds. I'm not sure how many people thought of this or if crediting anyone is really appropriate, but Disco considered using RR to self-cleanse in the middle of our world's first raid attempt, and I think that's very clever of him to have noticed so early on.
And finally, my gf Vanessa who had to babysit our dog and let me game until odd hours of the night so I could run this to my heart's content.
A lot of others suggested I use Lament skating for speed, but I prioritized consistency with what I am already familiar with over gaining a small (but significant) boost to speed that would disrupt my intuition. Overall this run is so heavily dependent on speed that anyone attempting it should at a minimum be comfortable with everything I do, but mastering sword movement on top of that would be a big help.
Interesting details about the run:
I'm gonna start this section with the two most frequently asked questions:
In order to determine the correct replication without scanner, you damage the replications as much as you can without actually forcing wrong stun / wipe. By doing this, the correct replication will take noticeable damage on its health bar, but incorrect ones won't wipe you. This doesn't entirely eliminate RNG, but it increases your odds enough to make this a realistic challenge.
In order to cleanse the replications from yourself as operator, you use ricochet rounds against a wall in the airlock.
This is probably the most interesting solo we've ever had in Destiny in my opinion. Without necessarily speaking to its difficulty, it has everything a solo challenge has ever had in Destiny bundled together. There is a major RNG barrier to success (which I'll get into in much more detail), there is a incredible stress test on movement speed and precision, inaccurate shots are often punished with a wipe, and anything short of the best build quickly begins to push the run into the realms of impossibility. On top of these factors, the narrow timeline for elevator cleanses combined with the 4-stun DPS cycle seems to almost suggest Bungie deliberately intended to keep this option possible, which is probably just a coincidence, but it's amazing to see exactly how narrowly this challenge avoids impossibility. These opinions on the run are absolutely subject to change if any more strategic breakthroughs are found post-completion, but barring that possibility this challenge is the ultimate gauntlet of Destiny mastery and dedication. I'll expand on all those points one at a time, with RNG last because it's the most interesting to me.
On movement:
As far as movement is concerned, every wasted instant hurts your chances of success substantially during the DPS phase. An icarus dash without a boost, an unnecessary clamber, a bodyblock from a wretch, or any other miniscule thing is likely to reduce your chance of completion by 25% on the spot. This is because, while stuns in space are effectively a given when playing well, you need to immediately cleanse the replication in an airlock and get back to the orbital elevator pods afterwards. During this window, any tiny mistake robs you of a guess on the base side replications - hence, even very small movement hiccups can reduce chances of success by 25% (or 33% on the second stun). In my runs I was not able to test all replications at base side even a single time - even when my space side stun was as convenient as possible - so you're going as fast as possible to fight for the best odds, not to guarantee success.
Movement hiccups after a base side stun are a bit more forgiving, but small mistakes there can compromise your ability to pick a good position to stun from on space side. Ideally on a third stun you are able to stun from right next to an active airlock, but getting into position takes time - if you trip up base side, it's likely you'll have to stun from a suboptimal position for the third stun, which greatly reduces your chances of making it to the final stun in base afterwards. It's hard to overstate how impactful even tiny movement slip-ups can be in this run. Also, this run is subject to the typical nuances most Destiny challenges are, such as kicking around replication instead of picking it up for no apparent reason / sword attacks tracking to enemies instead of the direction you're facing / etc.
Again, noting that I'll go deeper into this later, RNG can make your forgiveness for movement better or worse, ranging from totally impossible, to absolutely unforgiving, to "please don't let me mess this up, this is as 'easy' as it gets." In so many words, your speed and precision with movement are major decisive factors in your odds of success.
On aim:
While there isn't much to say about aim, there are two critical points where accuracy is do-or-die. During any "testing" of the bosses, a missed grenade launcher shot is a major time waste. This, like with movement, can easily mean a single missed shot reduces your chances of success by 25% or 33% depending on the phase.
For example, if I am going base side after a good 3rd space stun and I've already killed close right base replicant, I still need to test two of the three remaining among left, middle, and far right base side replicants. The optimal way to do this is to stand by middle and shoot left without moving (as there's a 2/3 chance I'll be moving farther from the real one if I do, and I still have time to get to left if it's the correct one since it's my first guess), then shoot right while moving toward it (guess is now a 50/50 and I'm running low on time so I want to be as close to both options as possible), then either attack right or middle after I've ruled out the others. In this scenario, if I miss the shot on the left or right side replicants, my odds of success drop from 100% (4th stun is guaranteed by accurate gameplay following a good RNG 3rd stun) down to 66% on the spot. You simply do not have time redos with the base side stuns, so precision in aiming here is absolutely critical. Also, I assume it sounds easy enough to hit a boss with a grenade launcher, but it really is more difficult than it sounds, particularly when the boss randomly meanders behind partial cover.
As with movement, good RNG can help relieve the punishment of poorly aimed shots, but this is a challenge in which you want to rely on RNG as little as possible since you're forced to rely on it to a significant extent by design.
The second place where aiming is critical is during the cleanse. As stated before, you cleanse by using ricochet rounds against the airlock door while crouched for best consistency. What's not immediately obvious about this is that a slightly angled shot (left or right of direct center) will be a miss that doesn't cleanse you. Same with too high or too low. The cleanse happens as follows: get to airlock door-> crouch -> push yourself up against the door -> aim directly at it, at the right height, on center, without any clear visual indication you're aiming at the right spot -> fire -> trust you got it right and keep moving. The hitbox isn't exactly "tiny" per se, but it is absolutely small enough that your aim needs to be careful and deliberate every time you cleanse.
On builds:
I have never actually felt the need to perfect a modded loadout for my character before this challenge. During other difficult runs like GM nightfall solos I would use the necessities and wing it from there, but in this case, mods and gear were insanely important.
Kinetic weapon: Needs to be ricochet rounds. Strong preference for mid range (so not SMGs ideally) and high RoF (so not HCs ideally) for cleansing and optimal engagement range. Feeding frenzy is a valuable perk since you can't crit shanks, and rampage or swashbuckler are ideal damage perks since you don't want to waste time reloading (particularly wearing transversives, which improves speed and is therefore probably best). I used a False Promises that dropped in the middle of my early attempts in the most serendipitous imaginable fashion At the end of my runs, it had exactly 8000 kills on it. Prior to that I used a RRapid hit/Kill Clip Sacred Provenance, which was also very good and would have done the job similarly well.
Energy weapon: Needs to do exactly enough damage to clearly hurt the right replication but not cause a wipe on the wrong replications. Also needs to be usable at long range. Strong preference for mid-air accuracy for movement reasons. I used truthteller, and I'm honestly not sure anything other than a grenade launcher can meet all the demands of the challenge. As an incredible bonus, blinding grenades disable servitor immunity which makes them far less of a hassle to deal with (particularly because they can make the boss immune under bad circumstances), and greatly reduces enemy add pressure (which is one important piece of the "I feel invincible" build).
Heavy weapon: Ideally, Two Tailed Fox, but since I didn't have that, I used Lament. This is pretty much exclusively for boss DPS.
My armor mods were built around two key concepts: 1. maximizing DPS, and 2. staying alive. Shocking.
As far as staying alive, I used two arc resistance mods (which are insanely strong, I had no idea). This alone makes an enormous difference in the boss room because almost every bit of damage thrown at you is arc, so those mods are unbelievably high value for their cost. On top of that, I used the raid specific "enhanced operator augment" mod, which is absolutely busted. The way this mod works is that whenever you're down to red health, you get periodic bursts of health regeneration. I did spec into recovery for the most part as a general stat, but so, so many times I was hit hard enough to be low red only for this mod to kick in and bail me out. With the combined effect of high recovery, double arc resist, and op mod (an appropriate shortening), the enemies in the encounter almost never killed me. The sheer damage resistance and health resilience was actually hard to believe.
For the damage aspect, I exclusively focused on maintaining lucent blade. Since lucent blade doesn't stack with things like rifts, wells, or much else, this was an easy way to give my damage a strong buff without having to change gameplay to make it happen. I ran supercharged (+2 charge cap), charged up (+1 charge cap), and stacks on stacks (x2 charge gathered) to minimize the amount of intentional charge gathering I'd have to do, taking charge to get the charges (orb pickup = +1 charge), and lucent blade as the singular way to spend those charges. I would've loved to use protective light as well just to see how unkillable I would be, but anything that might take my charges before a DPS phase wasn't an option.
The rest of my mod slots were spent on things like grenade scavenger (artifact mod) and finder, which all helped, but ultimately weren't critical in the run. My split of recovery and mobility was tier 8 and tier 6, and those numbers also helped but were probably not critical.
On the uncanny "did Bungie intend this?" design of this challenge:
Replications are the most significant limiting factor to success in this challenge. You can only cleanse them in space, but you have to pick them up in base, too. This means that you'll definitely have times where you can't cleanse instantly after picking up regardless of your RNG - on the first stun you can, on the second you can't, etc. Here's why that's interesting - as a result of this design, the optimal pathing is effectively as follows:
1st stun / cleanse 1st / run base 2nd stun / run space 3rd stun / cleanse 2nd / run base 4th stun / run space
... so 5th would theoretically be
5th stun / cleanse 3rd / run base
at which point you'd be left with replications 4 and 5 uncleansed while headed the direction exactly opposite of where you need to be to cleanse them. If the DPS phase ran any shorter cleansing would be trivial, and if it ran any longer, it would be impossible. To me, this is quite a coincidence.
But that's not all there is to it - replication balls wipe you if left unattended for ~45 seconds, and this timer is reset when knocked off a player (notably, NOT when picked up). As a result of this, combined with the ~13 second elevator ride and pickup mechanics, this is the timeline of a ball juggle to get two balls to space at once after fourth stun:

Start by dropping 3rd at base elevator, resetting it
Grab 4th and drop it at base elevator to reset it, too
Pickup 3rd / go to space / drop 3rd / go to base
Pickup 4th / RESET 4th / Pickup 4th / go to space
Drop 4th / pickup 3rd / RESET 3rd / begin soccer

During this section, you have to leave stuns unattended for nearly their entire timer as you go up and down the elevator. As it takes some time to pickup the ball, drop the ball, and move between flat walls and the elevator pods - on top of the elevator movement itself - it's not unusual to run those timers dangerously low. This actually wiped me at least a handful of times due to going slightly too slow - it just so happens that my successful run was very clean in this regard so I had a very generous 6 seconds every time at least.
All this said to reinforce one point: A longer DPS cycle would be impossible because you'd have to juggle during a DPS phase, which there isn't time for, because the time allotted by the replication mechanic and elevators makes it so. In my opinion, quite the interesting coincidence if this is unintended.
And finally, on RNG:
This challenge is NOT a Riven solo. Riven's eyes was a 1/45 pure odds that you couldn't help at all, combined with the 1/2 chance of her even going to the right side for a start the fight. Riven solo was a beast of its own, but the true "difficulty" of it was almost entirely found in fighting against terrible odds. Atraks-1 is very different in the sense that, while it does have a large RNG component, there are a myriad of ways you can mitigate it and keep your chances of success far better than the 1/90 on a fresh Riven start.
By my calculations - which are summarized in this image link - you have a 29.3% chance of any run being completable at the outset, considering there is a 54.2% chance of success on any single DPS cycle, and you need two of those in a row.
The information in that image is subject to two major considerations:
Firstly, boss movements can totally let the goose loose. If a boss stands in a particularly terrible spot, it becomes much harder to check it and can reduce your chances of success by forcing you to guess with less testing either by committing to the weird spot or by ignoring it and hoping it's not the correct one. In any case, the overall good/bad value of the stun cycles I've covered are not absolute by any means.
Secondly, the testing order also matters a lot. The clearest example is that any run in which the 2nd stun is on base left I have considered an irredeemable wipe. This is because testing all four in base is nearly impossible, so it makes sense to prioritize checking base far and base close (both of which are on the right side), so there's a high probability of finding the right one. Because the limiting factor is travel time, you are forced to check "sides" rather than specific replications.
It's very possible that improvements to routing and testing speed could come later and improve the wipe / difficult scenarios to better statuses, but as of right now, the probability of it being a completable run is somewhere around 29.3% with the current knowledge - so in that way, it is a very RNG based challenge (as the variability in difficulty from the top end of that 29.3% to the bottom of it is very high), but as far as completability goes, it isn't just hoping for the best all the time. Rather, this challenge is entirely about overcoming bad scenarios.
Overall, I love this raid encounter, and while this challenge is very RNG heavy, and 75% is not 75% somehow, I enjoyed doing it a lot.
Also, HUGE congratulations to Tier 1's Vendetta who also just completed this challenge. Incredible achievement on his part and he was impressively quick to get a completed run in. Can't say I'm too surprised though, he's an insane gamer. Major kudos to him!

Le Bilan - Ligue 1 Matchday 18 : Angers Management

After a well-deserved two weeks break, Ligue 1 was back with a double 5-matches batch on wednesday night. With the situation as close as ever both at the top and at the bottom of the ranking, there was a lot of things to follow. And two attractions in particular : the first matches of Raymond Domenech for Nantes and Mauricio Pochettino for Paris.

Appetizers

The first fixture for Nantes' new coach wasn't the easiest, clearly, at home against Rennes. And while they logically suffered (only 28% of possession), they didn't concede too many occasions and it was mostly Rennes that was disappointing. After their great month of december, the red and black had the opportunity to get closer to the top 3. In the end, a rather poor 0-0, one of the three of the night.
At the opposite side of the excitation spectrum was the match between another breton team, Lorient, and Monaco. The sending off of Jonathan Delaplace early in the second half while the score was 1-1 made things easier for Kovac and his players who unleashed their attacking power (the 3rd attack of the league) to score 4 goals in less than 30 minutes, notably a beautiful one by Golovin, 8 seconds after he entered the pitch before being responsible for two other goals, four months after his last match. While the monégasques appear ready to go for a european spot, Lorient falls back in the bottom two in this joyous merry-go-round that is the relegation battle.
Patrick Vieira or not, William Saliba or not, the incremental changes made by OGC Nice don't seem to produce any positive results. Of course the absence of Kasper Dolberg isn't helpful but not losing to Brest should be the bare minimum expected from a team who has invested a lot recently. Anyway, Brest claimed their sixth home win this season with goals from Steve Mounié and former Nice player Franck Honorat (following a now traditional nonsensic defensive mistake).
Last time, we talked about Nîmes' alarming situation and how crucial the match against Strasbourg was going to be before a tough schedule ahead. Well, this is now DEFCON 1 after the thrashing they were the victims yesterday (and kind of perpetrators too when you offer three penalties to your opponents). Strasbourg hasn't looked like a relegation candidate for a good month now. Ajorque and Diallo is too good of an attack to be in trouble.
In a rather similar manner to a previous win this season in Strasbourg, Lyon seemed to be determined to search for unneeded troubles. There's no reason to panic when you are ahead by three goals against a promoted team, albeit a very good one, who oddly enough scored all their 27 goals of the season from the penalty box (stat provided by av1997f aka OptaGone). Yet here we are again with a torturous last 30 minutes full of fear and anguish for the fans. Fortunately, that ended well again (and especially given the other results) but piling on defensive players to protect a result is not necessarily a reassuring way of assuming your title chasing status.
If Lyon can be happy about their win against Lens, it's also because their rivals failed to produce the same result. And the first one did it pretty badly as Lille lost at home to Angers after an early brace from the center-back Romain Thomas. The northerners hadn't lost at home for nearly a year but the defensive quality of Angers prevented them to score more than one goal despite a logical dominance throughout the game.
The second one, of course, is Paris Saint-Germain and their shipment of injuries. Of course when 75% of the defense is composed by Dagba, Bakker and Kehrer, it's difficult to be confident. But it's Idrissa Gueye that offered les Verts and Romain Hamouma the chance to take the lead. The champions reacted quickly with a 100% italian goal (assist from Verratti to Kean) 3 minutes later. After that, a pretty even game with sequences of domination for both sides (for Saint-Étienne in the first part of the second half, for Paris later in the game). Obviously a disappointing result for Pochettino in his first match while Sainté is on a bizarre serie of 6 draws in their last 7 matches (and, more worrying, 1 win in their last 15 matches).
After a tough end of 2020, Marseille got back up against Montpellier in a match where 31 shots took place. The olympians were the most precise which allowed them to score three goals to one for their opponents, a logical victory after dominating rather clearly the match despite Benedetto's best efforts to throw his chances. It was Payet and Germain, recently subbed in, who delivered the win. Montpellier's european ambitions unfortunately seem to be shrinking week after week.

Main Course

Matches

Home	Score	Away
FC Nantes	0-0	Stade Rennais

FC Metz	0-0	Girondins de Bordeaux

FC Lorient	2-5	AS Monaco
Moffi 31', Gravillon 67'		Disasi 9', Golovin 64', Volland 68', Diop 78', Maripan 88'
Stade Brestois	2-0	OGC Nice
Mounié 23', Honorat 28'
RC Strasbourg	5-0	Nîmes Olympique
Ajorque 36', Diallo 38', Lala (p) 45'+1, Ajorque 51', Waris (p) 90'
Olympique Lyonnais	3-2	RC Lens
Depay 39', Fortes (og) 46', Depay (p) 52'		Sotoca 56', Doucouré 89'
Olympique de Marseille	3-1	Montpellier Hérault SC
Radonjic 41', Payet 80', Germain 84'		Mollet 52'
AS Saint-Étienne	1-1	Paris Saint-Germain
Hamouma 19'		Kean 22'
Stade de Reims	0-0	Dijon FCO

Lille OSC	1-2	Angers SCO
Yılmaz 42'		Thomas 6', Thomas 11'

Table

#	Team	Pts	P	W	D	L	GF	GA	GD
1	Olympique Lyonnais	39	18	11	6	1	37	16	+21
2	Paris Saint-Germain	36	18	11	3	4	40	11	+29
3	Lille OSC	36	18	10	6	2	32	14	+18
4	Stade Rennais	32	18	9	5	4	26	19	+7
5	Olympique de Marseille	31	16	9	4	3	25	16	+9
6	AS Monaco	30	18	9	3	6	33	27	+6
7	Angers SCO	30	18	9	3	6	25	27	-2
8	RC Lens	27	17	8	3	6	28	27	+1
9	Montpellier HSC	27	18	8	3	7	30	31	-1
10	Stade Brestois	26	18	8	2	8	30	31	-1
11	FC Metz	24	18	6	6	6	19	17	+2
12	Girondins de Bordeaux	23	18	6	5	7	18	20	-2
13	OGC Nice	22	17	6	4	7	21	24	-3
14	AS Saint-Étienne	19	18	4	7	7	19	26	-7
15	Stade de Reims	18	18	4	6	8	24	28	-4
16	RC Strasbourg	17	18	5	2	11	27	32	-5
17	FC Nantes	16	18	3	7	8	18	30	-12
18	Dijon FCO	13	18	2	7	9	12	26	-14
19	FC Lorient	12	18	3	3	12	19	36	-17
20	Nîmes Olympique	12	18	3	3	12	14	39	-25

1-2 Champions League group stage
3 Champions League qualifiers round 3
4 Europa League group stage
5 Europa Conference League play-offs
18 Relegation play-offs
19-20 Relegation to Ligue 2

Goals

Player	Team	Goals	This week
Kylian Mbappé	Paris Saint-Germain	12
Memphis Depay	Olympique Lyonnais	10	(+2)
Boulaye Dia	Stade de Reims	.
Karl Toko Ekambi	Olympique Lyonnais	9
Ludovic Ajorque	RC Strasbourg	8	(+2)
Andy Delort	Montpellier HSC	.
Moise Kean	Paris Saint-Germain	.	(+1)
Kevin Volland	AS Monaco	.	(+1)
Burak Yılmaz	Lille OSC	.	(+1)
Wissam Ben Yedder	AS Monaco	7
Tino Kadewere	Olympique Lyonnais	.
Gaël Kakuta	RC Lens	.
Habib Diallo	RC Strasbourg	6	(+1)
Gaëtan Laborde	Montpellier HSC	.
Ibrahima Niane	FC Metz	.
Florian Thauvin	Olympique de Marseille	.

Assists

Player	Team	Assists
Jonathan Bamba	Lille OSC	7
Florian Thauvin	Olympique de Marseille	.
Andy Delort	Montpellier HSC	6
Gaëtan Laborde	Montpellier HSC	5
Ludovic Ajorque	RC Strasbourg	4
Houssem Aouar	Olympique Lyonnais	.
Hatem Ben Arfa	Girondins de Bordeaux	.
Wissam Ben Yedder	AS Monaco	.
Memphis Depay	Olympique Lyonnais	.
Ángel Di María	Paris Saint-Germain	.
Gaël Kakuta	RC Lens	.
Kylian Mbappé	Paris Saint-Germain	.
Romain Perraud	Stade Brestois	.
Junior Sambia	Montpellier HSC	.
Pablo Sarabia	Paris Saint-Germain	.
Karl Toko Ekambi	Olympique Lyonnais	.
Burak Yılmaz	Lille OSC	.

COVID Championship

(May not be 100% accurate)

Team	COVID cases
OGC Nice	17
RC Lens	14
Montpellier Hérault SC	11
FC Nantes	10
RC Strasbourg	9
Paris Saint-Germain	.
Lille OSC	.
Olympique de Marseille	.
AS Saint-Étienne	7
Olympique Lyonnais	6
AS Monaco	.
Dijon FCO	5
Nîmes Olympique	.
Stade Rennais	.
Angers SCO	3
FC Metz	.
Girondins de Bordeaux	1
Stade Brestois	.
FC Lorient	.
Stade de Reims	.

Dessert

Top 3 Goals of the Week

#	Player	Match
1	Cheick Doucouré	Olympique Lyonnais vs RC Lens
2	Florian Sotoca	Olympique Lyonnais vs RC Lens
3	Romain Thomas	Lille OSC vs Angers SCO

Upwards

Angers SCO : Ever since they were promoted in Ligue 1 in 2015, Angers has always been a reliable midtable team. Neither pushing for a european spot, nor have they ever been under an immediate relegation threat, as proven by their finishing positions : 9th, 12th, 14th, 13th and 11th last season. Basically a french Burnley. They actually share other similarities with the english team. Both teams base their results primarily on a defensive and collective strength, nicknamed "la dalle angevine" ("angevine hunger") and both teams have their coaches leading the rankings of their respective leagues in terms of longevity (2012 for Sean Dyche, 2011 for Stéphane Moulin, the longest ongoing tenure in the Big 5 leagues). Currently, Angers is comfortably in the first half of the table following two wins against Marseille and Lille, no less. With the 10th attack and the 11th defense of the league, there is little reason to believe they will finish the season at a much higher position than where they are now but their 30 points tally may allow them to be slightly more ambitious in the second half of the season. After all, they've got a very talented player at their disposal. After Nicolas Pepe and Karl Toko-Ekambi recently, this year it's Angelo Fulgini who's the technical leader of the black and white team and by far their best player, as stated by EastOfEden_ here. Keep going Angers, you work very well !
Kevin Volland : After a little hiccup in early december (three losses in a row), Monaco has since bounced back with two wins and one draw (though it was the minimum expected against three teams from the bottom of the league). And there's one man who has been the symbol of Monaco's resurgence under Niko Kovač : Kevin Volland, who scored in each of these last three matches, increasing his tally up to eight goals since the beginning of the season and his arrival in the Principality, including two against Paris Saint-Germain during the 3-2 victory in november. The german striker has brought with him the consistency he had in Bundesliga and it's a good news for Monaco as Wissam Ben Yedder has lost a bit of influence recently following his COVID-19 positive test (he still assisted 3 times since then but scored only once compared to 6 goals previously). Volland has been reliable, clinical and technically vastly superior to the Ligue 1 average. His last goal, very instinctive, is a perfect example of his level of confidence right now. With the coming back of Aleksandr Golovin after four months of injury, the rise in power of Sofiane Diop and if Ben Yedder gets back his goalscoring ability but most importantly if they manage to settle their defensive issues, Monaco will be in the battle for the fourth place.

L'Équipe Team of the Week

https://imgur.com/a/z6Kt1mC

Quotes

Thierry Laurey, Strasbourg coach :

I had told the players before the match that you rarely win 5-0 on a restart match and that it is often in difficulty. I would have done well to keep my mouth shut.

Jérôme Arpinon, Nîmes coach :

I can't find the values of the club, and that bothers me a lot. If you want to be maintained, you have to show something else. Strasbourg has stepped on us in all the duels. Some have to rediscover values, you can't play with 8 or 9 players on the pitch, some pretend to run. [...] We don't feel that we're fighting to stay up. From now on, we're going to put on guys who may have less technique, but more values, more heart, who have a love of the jersey.

Stéphane Moulin, Angers coach :

Is it the most beautiful victory since the comeback in L1 in 2015? Yes, because we played against the tied leader, who was undefeated at home this season. We have achieved a great feat, it is a very, very big performance, I am very, very proud of the team and the group. If there was a good moment, it was this one: Lille played a lot of matches and there was a break. I had said there would be surprises, because the big teams are not necessarily ready.

Claude Puel, Saint-Étienne coach :

There is satisfaction for our general behaviour and a bit of frustration because we have the feeling that we could get a better result. But we mustn't be too greedy. This feeling was stronger in the previous matches that we deserved to win. But we are on a good phase, even if we are not rewarded. We saw a structure, playing intentions, presence on set-pieces, we took the ball out cleanly. We have to continue like that.

Christophe Galtier, Lille coach :

Angers deserves its victory but we were absent. Absent without the ball, absent in the marking, absent in the duels and with too much technical waste. Maybe this defeat will remind us what we have to put into a match from the first minute to be performant. Losing matches can happen, but by being so absent, there are questions. This defeat doesn't worry me, I don't want to ring the alarm bell. But it does make you think. We have to ask ourselves the right questions. We had to do much better.

av1997f, statistician :

28% of possession for Nantes. Whatever the way we calculate the possession, we can say they don't play like Barcelona.

Next matchday

Saturday 09/01, 21:00
Dijon FCO - Olympique de Marseille
Girondins de Bordeaux - FC Lorient
RC Lens - RC Strasbourg
Montpellier Hérault SC - FC Nantes
Stade Rennais - Olympique Lyonnais
Stade de Reims - AS Saint-Étienne
Nîmes Olympique - Lille OSC
FC Metz - OGC Nice
Paris Saint-Germain - Stade Brestois
AS Monaco - Angers SCO
Thanks a lot to Hippemann and NotMeladroit for all the clips and the tables ! For more news about the best league in the world (except for the other four) and to improve your french, come and subscribe to /Ligue1.
All feedbacks are welcome !
Previous matchdays :
Season 2020-2021
M1 - M2 - M3 - M4 - M5 - M6 - M7 - M8 - M9 - M10 - M11 - M12 - M13 - M14 - M15 - M16 - M17 - Mid-Season
Season 2019-2020
M12 - M13 - M14 - M15 - M16 - M17 - M18 - M19 - M20 - M21 - M22 - M23 - M24 - M25 - M26 - M27 - M28

Reclaiming What is Mine

I was about seven when my dearest friends were the sisters who lived next door. We lived at the end of Yuba Avenue in San Pablo, California, and spent afternoons together playing pretend of various sorts. We gathered stones and figs from the ground and made up all kinds of games with them. I was always the adopted one because my skin was significantly lighter than theirs. They were black. I was white.
We weren’t really those colors but that’s what people liked to say. I would have called us peaches and almonds, but no one ever asked me. I watched their mother braid their hair into fantastical creations, using more beads, barrettes, and bobbles than I’d seen in my life. It was incredible to watch her hands knitting their hair into braids that would clack together during a rousing game of jump rope the next day. It smelled like coconut and was soft like snow. I envied their hair with every ounce of my body.
My hair was straight. And brown. And didn’t ever do anything like theirs did. It smelled like Pert Plus. On Sunday nights my mom would braid the ends and I’d sleep in them and my hair would be wavy the next day. I wore headbands, but they hurt. It wasn’t the same. I wanted their hair in all of its majesty. ( ^Monique Wells this is the full story for your daughters.)
One afternoon we were playing over at my house. They started signing “black is beautiful, black is beautiful.” It was a quote from a recent TV show that I hadn’t seen, so I ignored it, but their intensity grew. They fed off the energy it gave them and began taunting me in a sing-song way, tilting their heads from side to side saying “black is beautiful, black is beautiful” and I knew I’d never be as cool as them so I ran to my mom crying. This lead to my mom and her mom having an in-depth discussion of race that I wasn’t really prepared for being about seven years old and all.
It caused a rift between our mothers that probably never healed. Both held positions that made complete sense. A mom doesn’t want her child to be taunted, another wants her children’s ethnicity to be validated without question. Makes total sense if you think about it. Eventually, they moved to a nicer house in another nearby neighborhood and I saw them less and less as we grew older.
When I was in sixth grade, I attended a mostly black and Latino middle school. I was about five feet tall, flat-chested, new to wearing jeans, and terrified at all times. It was a very difficult year for me. I was often mocked and taunted by random students, and also targeted by specific ones (a story for another day titled “My First Sexual Harassment Stories” it’ll be suuuuuper fun. Subscribe to my Patreon!)
One day I remember having my ponytail jerked down as I was navigating a packed staircase. I whipped my head around, to find only much older girls laughing at me and how stupid I looked. I’m sure they were right. And I’m sure they were hurting. They probably had a much more difficult life than I had. Many of my fellow students seemed to be raising their younger siblings or navigating difficult social situations with older siblings or both. Being eleven isn’t easy for anyone. At least no one that I know. I was a white, only child whose parents had a blossoming business in electronics. I didn’t have the same challenges. I had my own.
We moved to Davis, California the next year. I had no idea how to handle myself in a room dominated by white people. It was not something I’d ever experienced before. It was a level of stress and anxiety that was brand new and never really left so long that I lived there. I was mocked and bullied there too, but it was more personal. They attacked the length of my pants as I sprouted up to 5’8’’. They mocked my favorite movie. (Labyrinth. I’m sorry, but if you don’t think that movie is cool, that’s an issue with YOU.) My cheeks, my chest, my grades were all subject to scrutiny. I was called Gerbil Girl for over a year for really no good reason at all. I wish they would have just pulled my hair and gotten over it.
I didn’t enjoy Davis. I probably should have found it less painful than I did. I honestly hated it. I’m not exactly sure why. The whole attitude of the place gets under my skin. But many of my formative years happened there so I guess it is what it is. It didn’t deserve all the mental anguish I gave myself about it. But I will never live there again, no matter how many times my husband innocently suggests it.
At the time, Davis was lily-white with a sizable Asian population, mostly centered out the university. The racial makeup with the exact opposite of where I came from. I enjoyed the years and the people I chose, but I always held contempt for the locale. It was too hot, too dry, too stuffy, and obnoxious. It’s a nice place for other people. I highly recommend it if you don’t hate it.
Anyway, in my senior year of high school, there was an emerging trend of white students asking for the space to explore their cultures as had been started by black student unions, Asian student associations, etc. They had barbecues and cool assemblies and fundraisers for cool field trips. They got to put it on their college admissions, (which was really important for some reason I never actually understood.) White students wanted a piece of that pie. We wanted to learn about our cultures too. Germans, Scots, Norse together selling bratwurst and haggis on the quad! How fun! We can wear lederhosen and kilts!
A group of three of us petitioned to make a club and we were referred to the vice-principal (a white lady) who told us that our idea “screamed white supremacy” and that it “just wasn’t going to happen.” My compatriot and I looked at each other shocked. We had no idea what our idea had to do with white supremacy. We just wanted to have fun and learn about ourselves, as teenagers do. Our idea was permanently axed and we went on our way, wondering what lesson we were supposed to have learned from that.
I spent a few years going to college and working in the neighboring town of Woodland. I felt much more at home in a Latino dominated community. Where I didn’t understand much of what people said and I happily smiled in blissful ignorance and delight of the beautiful colors and foods their culture brought. It felt like home in San Pablo, where I once was the only non-Spanish speaker on a soccer team. (Another one for another day— A Love Letter to Latino Communities perhaps.) I once tutored an elderly Mexican man who regaled me with stories of working in sugar fields. I taught him how to use a TI-36x calculator in what he thought to be a very generous exchange.
I reeled with rage when I learned about the cops harassing the son of my classmate at community college. She was a fantastic black woman— entrepreneurial and interesting, she was definitely a matriarch. Her home had been broken into by police and her young teenage son had been dragged out of his home and booked for suspicion of something or something something gang activity. It sounded like a load of bull shit to me. It should go without saying but I don’t trust it to so I’ll say— this young man was not involved in any gangs. There were not gangs in Davis. So what the fuck, Davis? Oh, and we lived in the same apartment complex. This all happened nearby while I was sleeping or playing video games or something. I didn’t even notice.
And then there was Halema. She was a Muslim girl who was a few years younger than me but went to the same schools. I never met her but had I been younger, we probably would have been friends based on descriptions I’ve read. Anyway, she was randomly accused of a traffic collision in a parking lot, basically, they accused her of hitting a parked car. So of course, the police BROKE INTO HER HOME and DRAGGED HER OUT OF BED. Nearly nude, terrified. Exposed. Humiliated. What the fuck. No. This is not okay. This is not an okay way to ever treat anyone. Especially not someone who values family and modesty so much. It’s the most disrespectful thing that I can think of. How could this happen so nearby?
We are not safe from these horrors in any community. This happened in Davis, California in 2005. You can look it up. It made the news and subsequent lawsuits. It’s super gross. And I’m sorry that any of that ever happened to anyone. Ugh.
That summer I found my passion for plant biology in a greenhouse in Woodland and was whisked away to another place a year later — Humboldt State University in Arcata, California. I was in love with the ocean and the trees, and I had the grades easy peasy, it was my ticket out of Davis. I peaced-out to another lily-white town but with a much better attitude.
I found all kinds of love there. Wrapped in a bundle of chlorophyll and spores, I wove myself into a new kind of life. I loved the Beatles and Pink Floyd, and I wore scarves in my hair and baby t-shirts and sneakers that had a little tiny pocket that was only good for three things. I had cannabis for the first time. I was deeply alive and extremely bright. Flourishing in all the cells in my body. I learned Calculus and started a social movement to get our university apparel made in factories with ethical standards. But that was only after Ethnic Studies. In Ethnic Studies, they tried to teach me about white privilege. I didn’t get it at first. I hated it. It was the worst. It meant I was the worst and I wasn’t the worst! I understood! Because I’d felt it so long ago. But no…. It wasn’t the same? I was confused. But I loved people. I loved every bit of every person I’d ever met. So how could this be on me? But it wasn’t. It was about me, but it wasn’t about me. But I needed to do something, but no one could really tell me what, but it was the message I needed in any case.
I was very confused. It didn’t make much sense until I relaxed and listened. Now I understand. I understand it all. I see what they meant. I see what they mean. We have to do better. We do. It’s not acceptable to live like we’ve been living. We have to be more accountable. We have to be better.
The damage that colonialism has done it has done to all of us. We all need to heal.
White people have a lot of work to do. We’ve already done a lot just this year. I’m actually really proud of that. I feel like this year was the first year in the last 14 years that I saw white people start to be accountable, start to chip away at the bullshit, start to actually do things that mean things. I think the ball is rolling. I think it will keep rolling and get faster and we will get there. We will have a just society. I believe it, my friends, I do. I think we are going to be okay.
We all need to heal. Together, and separately. In groups, and as a whole. We need to heal this wound. This fractured damaged part of us that is limited by old beliefs. We need to take our compatriots by the hand and lead them to this better life. We can’t keep defending the past, let’s go forward and on. That’s what makes me a progressive. I want to move toward the promised land. Come with me. We have work to do inside of ourselves. Deeply spiritual work that is uncomfortable but necessary and rewarding. We must do it. It is not an option not to. You have already begun.
I have spent the last ten or fifteen years reflecting on what it means to be a white person in America. What it means to be of a privileged class regardless of how we were treated. What we can do to help. What is ours to own and to undo? I’ve spent a lot of time on those questions. Now I still want to know who I am.
At some point in college, I asked about my own culture. Where was it? What happened to it? Where was I supposed to find it? I didn’t have a culture!!
Someone answered: It’s everywhere. You just can’t see it.
Someone was right. It is everywhere, but it is quite hard for me to see.
We call people who don’t know anything about plant taxa as being afflicted with the “green blur.” Every plant looking like just something green without any definition or distinction from its neighbors. But with training, it is a condition easily cured. A natural curiosity and a wondrous mind easily triumph over this lazy tendency. You can do it. You are in the driver's seat. We can do it. We can cure ourselves.
But why couldn’t I see my own culture in the consumerist colonialist murk? What kind of glasses did I need? Rose-colored? Probably.
So after my near divorce and all kinds of inner collapse and completely losing touch with my fern cocoon, I found myself again.
Hello glory, Bonnie 3.0 is launched. I’m not sure of the exact delineations but Juliana Sage, herself said earlier that I have evolved so haha! Here I am! Flourishing!
With my golden fire embers, and my two perfect children, and my most perfectest husband, like, holy moly, I did it. It was both the easiest and the most difficult thing. I’ve gone through so much yet so little comparatively.
With my life in order, I started to drive again, seeking to cure myself of my own cultural blindness. I started to ask questions… a lot of questions. So many questions that I won’t even try to begin to list them here, but their subjects consist of metaphysics, feminism and the divine femme, relationship strategy, spiritual coupling and growth, love as a catalyst, surrender as a path to God. And my favorite: Christianity Reimagined for Us.
I call to my ancestors to lead me back to wherever we were when we lived in rhythm with the earth, when we sang songs by firelight, when we grew herbs to keep ourselves happy. When we recognized the magic that flows through a mother’s fingertips.
I want it back. It’s mine to claim. I have it now. And this time I won’t be burned or shackled or strangled for practicing my magic which is not witchcraft in one sense but also is in another. It is the holy spirit, a gift to me, directly from God. I am honoring my father and mother as the Ten Commandments command. I am not a sorceress. But I am magical. I bet you are too no matter who you are. You just have to find it.
I dance free to the songs that sing in my heart in the Meadow and teach my children about the fairies that sit on the mossy oak ledges when we aren’t looking. That's how we find it.
I see it now in fantasy, fae, oak trees, harps, long dresses, and long wavy hair that never knows what it is or what it wants. It’s woven into flower crowns, wreaths on my door, tea that I grew and brew. It is in the love songs that make me cry of a land so far away. It’s in the ancient sacred symbols that still appear and give me pause. I see us, but it’s so hard to see because we have lost our way. Dungeons and Dragons is a good place to start. How odd. It’s in the patterns that come out at Christmas time and are worn by little girls. That’s my family, we are the royalty that brought you Christmas plaid. I wear a shawl made in faraway lands of the patterns of my people. It is not ideal, but it is all I have today.
What did my indigenous culture look like before the thousands of attempts by violent, patriarchal domination to systematically kill and erase us? It’s all starting to come together. All of that was misaligned. It was not how Christians should have ever behaved. A wise man once sang “Jesus don’t like killin’ no matter what the reason’s for.”
This time, we will not be condemned. The reign of fear is over. Let us bloom where we are planted. We are righteous in our reclaiming.

DWT26 (November 21st 2020)

A statistical look: 3 "unheralded" creative options (Luis Alberto, Ruslan Malinovskyi, Marcel Sabitzer)

tldr: A statistical analysis of some lesser known/discussed creative players. I've bolded my key points in the post if you don't want to read it all. Quick hit summary of my findings in the comments if you don't want to parse through the body of the post for the bolded sections.
Since the Premier League season ended, I've been working on some data analysis of players from the big 5 leagues to see if I can spot some objective insight on players we're interested in or unheralded names that I think would be good value. So far, my database is set up to analyze "creative players" and I kicked off my player analysis with Emi Buendia and Willian/Coutinho.
As you can tell from the title, this post is going to focus on a few players in the big 5 plays who haven't been discussed much on this sub, but who stood out during my analysis. Because I don't want to get too long-winded, I'll only cover three players here. I actually had planned on also including three U25 creative players and had finished a good bit of the write-up on them, but the post was getting really long so I'll save them for now and throw it up in another post in a couple of days.
Quick disclaimer: stats are never the whole picture - they can be inaccurate, be made to be misleading, and are best used in conjunction with watching game tape. To the extent that I can, I will try to cover the stats in a holistic way.
METHODOLOGY (skip if you don't care or have read my other posts):
Simply put, I compiled a database of all available statistics on players from the 5 big leagues (via fbref), then used the data to create a peer analysis. Once you determine your peer groups (I used Wingers/Attacking Midfielders, as categorized by fbref, with over 810 mins logged this season), you normalize each player's stats so that it's relative to the peer set. By doing that, I can see in what percentile a player is in relative to that data set by using the mean and standard deviation of that set (generally better than simply creating a % by saying "he ranks 20th out of 100 players in the set, so he is in the 80th percentile).
It's important to note that there are different ways to do this, with one of the more simple ways being standardizing it to a normal distribution, and this is what I generally did. But this only works when the underlying data is distributed like a normal bell curve, which it isn't for a lot of the cases. I tried avoiding using the stats which didn't apply well to the normal or lognormal distributio, but in cases where I do make reference to one of them, I provide an * to indicate that there may be some skew here. Those stats metrics are typically directionally correct, but have room for error in terms of percentile (anecdotally ~5-10% variance).
Explaining the peer groups: "Creative players" in this case refer to players categorized as FW/MF (both, not either or) by fbref. I'm not sure how fbref determined the player positions, but based on the pool of players, it seemed like most wingers and attacking mids were given both the FW & MF designation. It also made more sense to sort by FW & MF designation in my database because I didn't want defensive mids or strikers skewing my distributions of creative players. This did lead to some players who are regarded as "creatives" not being included in the data set (i.e. de Bruyne as MF; Mahrez as a FW), but with 270 players and most of the "world class creatives" classified with both the FW & MF designations, it made for a sufficient enough data set size to determine the mean and standard deviation (as that's what I use to determine the percentiles). I was able to retroactively apply the mean and standard deviation of the 270 player creative data set to all Mids and Forwards and determine their percentiles in each stat as if they was a FW/MF so that I could catch players who are creative, but not categorized as both FW/MF.
Note #1: Read "94.0% (10/273 ; 30/992)" as "in the 94th percentile for the stat, ranking him at 15th out of 273 in the "creative player" data set and 30th out of 922 in the all midfielders and attackers data set". I would recommend not focusing too much on the rank in the "all midfielders and attackers groups" - while useful in the above overall rank to capture the creatives that are weirdly categorized, it's not so much on an individual stat basis as midfielders and attackers tend to dominate in certain stats and under-perform in others relative to the average creative player.
Note #2: The lower the percentile, the worse it is, and vice versa. The percentile is calculated based on the standard deviation of the distribution and the distance of the individual from the mean, whereas rank is relative to how they directly stack up with their peers.

PLAYERS:

Luis Alberto (Lazio, 27 yr old) - Transfermarkt: £39.60m
- Overall Ranked as 7th out of 273 "creative players" and 11th out of all 922 midfield and attacking players

	Percentile
Chance Creation: Goal CC p90* / Shot CC p90	94% (10/273; 17/922) ; 97% (7/273; 14/922)
Goals p90 / xG p90*	52% (154; 382) ; 43% (138; 339)
Non-Penalty Goals p90* / Non-Penalty xG*	50% (167; 401) ; 40% (149; 357)
Assists p90 / xA p90*	96% (8; 16) ; 93% (12; 19)
Passes Attempted p90 / Pass Completion %	99% (6; 65) ; 77% (70; 372)
Key Passes p90	96% (9; 13)
Completed Passes Into Final 3rd p90	99% (4; 26)
Completed Passes Into Penalty Area p90	96% (7; 13)
Completed Crosses Into Penalty Area p90	30% (202; 489)
Completed Progressive Passes p90	100% (3; 9)
Passes While Pressured	96% (11; 50)
Touches p90	99% (7; 63)
Dribbles Attempted p90* / Success %	45% (143; 236) ; 84% (41; 277)
Dispossessions p90* / per Carry*	63% (113; 602) ; 91% (22; 314)
Tackles Attempted p90* / Success Rate	54% (110; 509) ; 57% (114; 372)
Pressed Ballcarrier p90 / Success Rate	56% (117; 350) ; 87% (33; 187)
Interception p90*	63% (83; 470)

Key Takeaways:

Background: Soon to be 28 year old Spanish Central Mid / Central Attacking Mid at Lazio, where he's been exceptionally productive over the last three years. Was with Liverpool between 2013-2016, but never really broke into the first team after couple of loan stints in Spain. 6 feet tall. Two years left on his contract per transfermarkt
Truly on of the elite pure passing creators. Alberto seemed to have been a very important reason for why Immobile won the Golden Shoe, as his incisive passing resulted in many balls being played into the danger areas to shoot and score. While I can't speak specifically to the efficiency (completion percentage) for each of the individual danger passing metrics due to lack of data there, his overall passing numbers show him to have among the most pass attempts, while maintaining an above average overall completion %, so there's a decent chance he was also relatively efficient on the incisive pass attempts as well. I will say that I also found his high percentile for passes made (not necessarily completed) while pressured to be somewhat surprising given Serie A's reputation of being low press (which was somewhat affirmed by a quick, cursory look at some overall stats, although it also says La Liga presses the least).
Pretty average when it came to shooting and goal output this last season, although with his high assists and passing efficacy, the fact that he can chip in goals is a bonus. While I didn't note the stats in the table, his shooting volume is actually above average, however, his shot on target as a % of total shots was below average, along with goals per shot and per shot on target, which probably has more to do with the quality of the shots that he's taking than a need to be more clinical (as his goals and xG are pretty close). I would note that his 17/18 season saw a lot more goals and better overall efficiency, so he may have it in him to be a better goal scorer.
On the ball a lot between the boxes - not an extraordinarily direct dribbler, but seems to be very successful when he does try and is quite secure with the ball (while his dispossessions p90 is moderately above average at 63%, it's function of how much he is on the ball; on a per carry basis, he's in the 90+% territory, which is very good).
Pretty average as a defender, with most of his percentiles for take-ons and success rate for tackles, vs dribbles, and interceptions sitting between 40-60%. Average when it comes to pressing (predominantly in the defensive and midfield areas), but had above average success when pressing. Might just be a byproduct of Serie A where players don't press very much and I question how well the efficiency would translate to the EPL.
And I didn't show the stat above, but Lazio seemed to be a better team when he was playing, as both his team net goals p90 (+/-) and team net xG p90 (+/-) while on pitch vs off were both above 90%. I don't take a ton of stock on this stat, but the fact that both those metrics bear out such a high percentile makes me feel better about considering this conclusion.
Overall: Luis Alberto seems like a fantastic creator with good ball carrying ability, who can chip in with goals and average defensive ability. I'd see his name pop up sometimes on soccer and all the Serie A fans would gather and say he's one of the best midfielders in the league and that he was very underappreciated. Based on my statistical look at him, I'd have to agree. I do have some concerns about his athleticism, moving from a press-lite league in Serie A to the press heavy and physical EPL, and age (will be 28 next month), making his quoted transfermarkt price of £40m to be a bit expensive, especially considering player's like Thiago are rumored at £40m, Aouar at £60m, Partey at £50m. But at a lower price (and his cheap contract), I think he'd be a good deal and would really help bolster our ability to create chances.

Ruslan Malinovskyi (Atalanta, 27 yr old) - Transfermarkt: £9.90m
- Overall Ranked as 12th out of 273 "creative players" and 16th out of all 922 midfield and attacking players

	Percentile
Chance Creation: Goal CC p90* / Shot CC p90	80% (44/273; 95/922) ; 99% (4/273; 18/922)
Goals p90 / xG p90*	90% (21; 87) ; 67% (76; 233)
Non-Penalty Goals p90* / Non-Penalty xG*	90% (16; 64) ; 60% (92; 257)
Assists p90 / xA p90*	67% (106; 238) ; 91% (14; 24)
Passes Attempted p90 / Pass Completion %	93% (23; 155) ; 75% (79; 388)
Key Passes p90	91% (27; 40)
Completed Passes Into Final 3rd p90	90% (29; 221)
Completed Passes Into Penalty Area p90	95% (8; 17)
Completed Crosses Into Penalty Area p90	83% (43; 89)
Completed Progressive Passes p90	97% (14; 59)
Passes While Pressured	88% (26; 114)
Touches p90	97% (17; 94)
Dribbles Attempted p90* / Success %	80% (48; 74) ; 93% (21; 185)
Dispossessions p90* / per Carry*	25% (213; 819) ; 75% (71; 464)
Tackles Attempted p90* / Success Rate	37% (160; 614) ; 67% (87; 275)
Pressed Ballcarrier p90 / Success Rate	44% (145; 466) ; 76% (61; 301)
Interception p90*	11% (251; 801)

Key Takeaways:

Background: Recently turned 27 year old Ukrainian Attacking Mid at Atalanta, arriving in last summer's transfer window. Was with Genk in the Belgian First Division A in the years prior to his move to Atalanta, where he seemed to play an important role in their title winning 18/19 season. 5 foot 11 inches (1.81 meters). Four years left on his contract per transfermarkt
A very strong creator who is able to contribute in multiple different ways. He may not be in the elite 95+% territory in most all of his danger passing metrics like Alberto, but he's very high in all of the danger passing per 90 metrics (even more impressive considering that his completed crosses into the penalty area are also in a very high percentile - very few players are able to be in the top quintile of every danger passing metric). I do find it a bit odd that (while still great overall) his Assists p90 trail his xA p90 percentiles and his goal chance creation p90 trails his shot chance creation p90 so much - Atalanta have had a reputation for being good finishers and only one of the other Atalanta playmakers I've looked at had this kind of disparity (as a side note, the stats really do bear out how good Atalanta was at creating chances this season, they're just on the older end, so I didn't include them in this write up). Again, while I can't speak specifically to the efficiency (completion percentage) for each of the individual danger passing metrics due to lack of data there, his overall passing numbers show him to have among the most pass attempts, while maintaining an above average overall completion %. And I'll explain this in greater detail in a bit, but he doesn't just create goals and shots chances through passing, he's able to create his own shots through dribbling and shooting.
Malinovskyi is also quite a good goalscorer in his own right, albeit he did overperform on his xG quite a bit. While I didn't include the stats in the table, his shot volume is very high, with average shot on target % and goals per shot and shot on target (again, can't necessarily determine shot quality or efficiency from just this, but I'd venture a guess that he's taking tough shots / shooting from outside the box a lot, driving his xG down - but I don't have enough data to state this definitively). Regardless, the fact that he is a goalscoring threat complements his dangerous passing excellently.
On the ball a lot, predominantly in the midfield and attacking area. A pretty direct dribbler who is very successful - a really nice combo as he can get behind defenders to spring openings in attack. His raw dispossession number is below average, but again, it's function of how much he is on the ball and the fact that he plays higher up the pitch where more defenders are on him. On a per carry basis, his dispossession rate is actually above average. That pairing of great dribbling and solid ball security make him a very good player in possession.
Overall, average as a defender. His take on numbers are a bit below average, but his rate of success is above average. Doesn't contribute much with interceptions either. You could maybe seeing this as a sign that he wouldn't be a great fit under Arteta, but I think it's more of a Serie A / Atalanta thing to not be press heavy. I wouldn't write him off at Arsenal because of that. It is good to see that at least his success rate stats are generally above average.
Overall: Ruslan Malinovskyi looks like an incredible value option, with a phenomenal combination of passing/assisting, shooting/scoring, and on-ball possession, all at a bargain price of ~£10m per transfermarkt (although I am very skeptical of that number given Atalanta bought him for £12m last summer and he has performed well this year - regardless, I feel like I'd easily spend more than that for him). I have to say, I think Malinovskyi seems like a really great option for Arsenal, really great creativity, decent age (just turned 27), and low cost. Plus I love saying his name. Anyone have Edu's number for me to pass this on?

Marcel Sabitzer (RB Leipzig, 26 yr old) - Transfermarkt: £31.50m
- Overall Ranked as 14th out of 273 "creative players" and 20th out of all 922 midfield and attacking players

	Percentile
Chance Creation: Goal CC p90* / Shot CC p90	68% (92/273; 188/922) ; 74% (73/273; 119/922)
Goals p90 / xG p90*	78% (55; 191) ; 57% (102; 278)
Non-Penalty Goals p90* / Non-Penalty xG*	80% (45; 151) ; 65% (81; 236)
Assists p90 / xA p90*	82% (51; 114) ; 65% (109; 210)
Passes Attempted p90 / Pass Completion %	93% (22; 153) ; 59% (118; 521)
Key Passes p90	82% (48; 96)
Completed Passes Into Final 3rd p90	92% (23; 177)
Completed Passes Into Penalty Area p90	90% (24; 43)
Completed Crosses Into Penalty Area p90	77% (69; 140)
Completed Progressive Passes p90	97% (11; 54)
Passes While Pressured	79% (50; 201)
Touches p90	91% (29; 169)
Dribbles Attempted p90* / Success %	11% (254; 623) ; 73% (78; 390)
Dispossessions p90* / per Carry*	84% (47; 421) ; 90% (24; 322)
Tackles Attempted p90* / Success Rate	48% (127; 549) ; 83% (45; 126)
Pressed Ballcarrier p90 / Success Rate	35% (174; 561) ; 100% (3; 9)
Interception p90*	84% (43; 375)

Key Takeaways:

Background: 26 year old Austrian versatile Attacking Mid (can play central and wide) at RB Leipzig, where he's been since 2014. He's been a staple in their lineup since the 2015/16 season. 5 foot 9 inches (1.77 meters). Two years left on his contract per transfermarkt
A good creative passer who is able to contribute in multiple different ways. Like Malinovskyi, not in the elite 95+% territory in most all of his danger passing metrics, but very high in all of them nonetheless. I can't speak to the efficiency (completion percentage) for each of the individual danger passing metrics due to lack of data, more so this time since his overall passing completion percentage is more in-line with the average. His Assists, xA, and goal/shot creation is a bit lower than his high percentile danger passing metrics would indicated to me, but they're still above average overall.
Sabitzer is proficient goalscorer as well, but again not elite. While I didn't include the stats in the table, his shot volume is above average, with average shot on target % and above average goals per shot and shot on target (again, can't necessarily determine shot quality or efficiency from just this). His G+A and overall chance creation isn't as good as Malinovshyi, but is still above average overall .
On the ball a lot, but the stats would indicate that he also gets more touches in the deeper areas (defensive third and mid) than most creative players, making him quite important to Leipzig's build-up play and ball progression. Not a direct dribbler (maybe due to opportunities if he's playing deeper to retrieve the ball), but who experiences above average success when he does try it. Very secure in possession, both from a raw and per carry perspective - trustworthy to have as your key ball progressor.
As a defender, average in terms of take-ons, but very effective at winning the ball when he does. Also on the high end with interceptions p90 as well. While he might not be the most press heavy player, his efficiency and interceptions make him a valuable contributor and maybe Arteta could get him to press more.
Overall: Marcel Sabitzer is a great all-around attacking mid, with above average ability in every area of his game. While he may not be as much of pure creator, part of that may be due to the fact that he's tasked with coming back and bringing the ball from deeper areas. I think he would be great for Arsenal as a ball-progressor, incisive passer, and goal contributor, while providing efficient defensive defensive production. He has a good age profile and should be hitting his prime years now, so ~£30m seems quite good for him if transfermarkt has his market rate pegged right. Based on the stats, I see him as similar to Willian, but better overall. To that end, maybe our resources would be better used to secure a different kind of player, but you shouldn't shy away from quality if the price is good (plus Sabitzer is versatile and we don't know how long Willian will keep up his level of play). And if you watched the Leipzig Champions League game against Atletico Madrid, you likely saw how much of a positive influence he was on his side.

Which part of the UK produces the best footballers? (Experiment) (Part 1)

After taking inspiration from u/Mel0n_collie world cup thread, I’ve set up a similar experiment which I’m planning to simulate on Football Manager 2020 which has kept me busy over the last few weekends during lockdown.
Hopefully, this experiment will provide the answer to the following questions:
Which part of the UK produces the best footballers?
Where do the real footballing hotbeds lie in the UK?
What if the Premier League was split into 20 teams representing UK regions of equal population?
Essentially, I’ve split up the UK into 20 equal(ish) geographical regions, with the idea being that each region has a team made up of players born in that region. I’ve used this website which shows the population of each county, I then calculated that each team should be made up of players from a region of 3.26m people (65.171m/20). Teams are filled up with whole counties, except for Greater London which has been split up into 3 regions due to having around 9m people in total. Note: Due to the diversity of the county populations and the challenges of geographically dividing the UK, some teams have slightly more/less than the aforementioned 3.26m people.
Split of Counties into Teams
https://preview.redd.it/2ney8b7fobz41.png?width=777&format=png&auto=webp&s=664a8a135dc29363406a819c89c296d339db1934
Here we have it, the 20 regions have been split up. Team 14 (Greater Manchester) have the smallest population but hopefully the produce of the academies of Premier League giants Man Utd and Man City will come to the fore. Team 7 has the largest population, but it remains to be seen whether this will translate into footballing ability just yet. I have also made some maps using my below-average paint skills to help visualise the split geographically.
I picked each team using a database of players from Football Manager 2020. I extracted a list of players into excel and began manually allocating each player to a team based on their city of birth (according to FM). Ultimately, I ended up on a squad of 25 players for each team and have tried my best to allocate an even positional spread throughout each squad so that the AI managers aren’t forced to play any ridiculously attacking/defensive formations. Squads have been loosely picked around current ability, reputation and transfer value.
Each team will be managed by a manager born from the region they are managing. I’ve allocated the manager with the highest reputation in each region according to FM to each team. This will add an extra element to the experiment and will be to interesting to see how much of an influence some of the better managers will have on their respective teams.
I will be posting the teams in groups of 5 over 4 posts, I will also be running a predictions game when all the 20 teams have been released so keep an eye out for that. I will then be posting the results of the simulation and prediction game on the fifth and final post.
Without further ado, I will reveal the first 5 teams one by one below in numerical order (I would also like some suggestions on team names for each region, as I am aware they’re not very creative!)
__________
Team 1 – South-West - Cornwall, Devon, Dorset, Somerset – Population: 3,450,896.00
Media Prediction: 16th - Title Odds: 350-1
Manager – Darren Way (Plymouth, Plymouth U18)
https://preview.redd.it/4ectj9vpobz41.png?width=510&format=png&auto=webp&s=2d967a07ea6011c4cf8ea7f6ac9a2ee83fc44d0e
Likely Starting XI
A region famous for Pasties, Cheese and Cider, the South West has brought to the nation many good things but unfortunately footballers (and managers) don’t seem to follow that trend. I had to search long and hard to find a manager that was born in this region. I eventually found Darren Way, who according to FM is the current Plymouth Argyle U18 Manager who recently had a spell in charge of League Two side Yeovil Town. Predicted to be in the bottom five come the end of the season, this squad seems like it will need a manager with a bit more experience to do anything of note.
The squad lacks all round quality, especially fullbacks, lets hope Darren Way is impartial to a 3 at the back system. Stand out players include Leeds United defender Ben White who has had a great season in the championship this year, Tyrone Mings, who has recently won his first England caps and Austrian U20 capped Burnley striker Ashley Barnes who looks like the best attacking option in the side.
Amongst the rest of the team, Scott Sinclair and Nathan Dyer look like they’ll occupy the flanks and will be hoping to re-ignite the success they both had together at Swansea back in the 2011/12 season. Jack Butland and John Ruddy will be competing for the number one shirt, who along with Tyrone Mings, share the squads total England caps (11) between them. The team also some exciting young talent in Ethan Ampadu and Xavier Amaechi, who are both currently learning their trade in the Bundesliga.
Arguably the best player in the Championship this year to date, Ollie Watkins will be hopeful to add some more goals to his ever increasing tally.
I am not hopeful for this team, considering the lack of top players and with the inexperienced Darren Way in charge but I’m hoping he can prove me wrong!
__________
Team 2 – South-East - East Sussex, West Sussex, Kent – Population: 3,495,475
Media Prediction: 20th - Title Odds: 600-1
Manager: Lee Johnson (Newmarket, Bristol City)
https://preview.redd.it/bxyum58xpbz41.png?width=539&format=png&auto=webp&s=290fe752211ad1ae898e9eb0fe2fffe16538aee1
Likely Starting XI
Up next, we have the combined counties of Sussex and Kent, the South East of England. Taking the reigns is Lee Johnson, current Bristol City manager, who has done well in recent years in the championship. However, in terms of ability it doesn’t get much worse. The media have predicted them to be rock bottom at the end of the season and have offered odds of 600-1 for a title win for Lee Johnson’s men.
The squad does boast the experience of Gareth Barry, who is the record all-time premier league appearance maker (653) and has also been capped 53 times by his country will be a frontrunner for captain duties. Sunderland ‘til I die fan favourite Jonny Williams makes an appearance in midfield along with Solly March who looks to be the most promising player in the final third.
The goalkeeper spot looks to be an issue with not one of the three goalkeepers being the first-choice keeper in their respective championship club sides. However, the Brighton duo of Lewis Dunk and Adam Webster look to be a solid partnership at the back and will be hoping to limit the amount of shots at goal.
With weaknesses in goal and up front, the media’s prediction is probably warranted, lack of goals looks to be a big worry with none of the forwards seeming to have top-level experience. I would be impressed with Lee Johnson if he could lead the South-East to safety with the resources at his disposal.
__________
Team 3 – South Coast - Hampshire, Isle of Wight, Surrey – Population: 3,135,638
Media Prediction: 8th - Title Odds: 30-1
Manager: Neal Ardley (Epsom, Notts County)
https://preview.redd.it/myk6l7clqbz41.png?width=617&format=png&auto=webp&s=863311423567a60bc3fd4beca94decfb9384bea3
Likely Starting 11
South Coast is managed by former Wimbledon and Watford winger Neal Ardley, current manager of National League side Notts County. Another manager who lacks top level experience will be hoping to make his mark at a higher level.
This team has a bit of quality compared to the previous two and has plenty of premier league experience with a total of 10 players capped by their country. England Internationals Alex Oxlade-Chamberlain and Mason Mount have genuine quality in the midfield and will be hoping to create the supply for Danny Ings to grab plenty of goals. However, In defence, Calum Chambers is the stand out player out of an average bunch.
Neil Etheridge, who qualifies for the Philippines by virtue of his Filipino mother will compete with Southampton number one, Alex McCarthy for the starting keeper spot.
Described as a ‘fairly determined’ squad, South Coast have a lot of industry in the team with players like Matt Ritchie, James Ward-Prowse and Tom Cleverley and will be hoping that this translates on to the pitch with some good performances.
The overall verdict in this squad is that they have enough ability and experience to finish the top half and could even push top four in my opinion.
__________
Team 4 – South & West London - London Boroughs: Westminster, Kensington and Chelsea, Hammersmith and Fulham, Wandsworth, Islington, Camden, Brent, Ealing, Hounslow, Richmond upon Thames, Kingston upon Thames, Merton, Sutton, Croydon – Population: 3,369,408
Media Prediction: 6th - Title Odds: 9-1
Manager: Roy Hodgson (Croydon, Crystal Palace)
https://preview.redd.it/yyb6xy0erbz41.png?width=580&format=png&auto=webp&s=dd158544449df1eb805284797573675ff88a4196
Likely Starting XI
The first of the three London teams, which spans most of south and west London is managed by Crystal Palace’s Roy Hodgson. Roy has been a football manager since 1976 and has a wealth of experience to draw upon, including a four-year spell as manager of his country. Despite being at the ripe old age of 72, Roy isn’t the oldest manager in the league..
The squad, tipped by the media to finish 6th, is an exciting young squad with plenty of players tipped to be a big part of England’s future.
Premier League loanees Freddie Woodman and Jamal Blackman (who is currently playing at Bristol Rovers on his 7th successive year out on loan from Chelsea) will compete for the starting keeping berth.
Man Utd fullbacks Luke Shaw and Aaron Wan-Bissaka will almost certainly occupy their respective positions and will hope to offer an attacking outlet bombing on from their full back positions. One of England’s one cap wonder’s Steven Caulker, who currently plays for Turkish outfit Alanyaspor also makes an appearance in defence.
In midfield, Declan Rice and Callum Hudson Odoi, both recently capped by England are the strengths which the team will rely on whilst fellow youngsters Ademola Lookman and Steven Alzate who has twice been capped for Colombia will also be looking to make an impact.
In Attack, Euro 2016 hero Thomas Henry Alex (Hal) Robson-Kanu, Sone Aluko and Lewis Grabban will all fancy themselves to start.
The youthful squad will bring a lot of pace and energy to the league and Roy Hodgson will be hoping for a top six finish at minimum. I think the squad has enough depth and quality to potentially go all the way.
__________
Team 5 – East London - London Boroughs: City of London, Lambeth, Southwark, Tower Hamlets, Hackney, Bromley, Lewisham, Greenwich, Bexley, Havering, Barking and Dagenham, Newham– Population: 3,009,408
Media Prediction: 1st - Title Odds: 4-1
Manager: Chris Hughton (Forest Gate, Unemployed)
https://preview.redd.it/5xjrm7rlrbz41.png?width=467&format=png&auto=webp&s=d8a8181cad75f86378130f0ca1dc0c49dc7f517d
Likely Starting XI
The Media’s favourites, made up of London Boroughs including and east of City of London is the 2nd smallest in terms of catchment area but certainly doesn’t lack quality. Ex-Brighton manager Chris Hughton is in the dug out and has a strong team which looks to have Premier League level quality all round apart from maybe in the goalkeeper position in which Fulham’s Marcus Bettinelli appears to be the likely candidate to start.
An all premier league back four consisting of Nathaniel Clyne, Joe Gomez, Chris Smalling and Ryan Bertrand, share 74 England caps between them look to be solid partnership and will be hoping to keep as many clean sheets as possible in order to be in with a chance in the title race.
Key man, Jadon Sancho, one of, if not the most exciting player in the league, will be hoping to chip in with as many goals and assists as possible from wherever Chris Hughton deploys him. Jadon has 14 goals and 15 assists in 21 starts this year in the Bundesliga and should be uncontrollable for some of the defences he will be up against . Talented Ruben Loftus-Cheek will be hoping to remain injury free throughout the season and make a good partnership with Jonjo Shelvey who will be looking to dictate play in the middle of the park.
Bradley Dack, who according to FM is likely to play off Chelsea’s Tammy Abraham will be looking to provide enough chances for Tammy who should have enough with the level of creative quality surrounding him.
Overall, A very strong team who will be hoping to keep Jadon Sancho injury free all season. Probably rightly favourites for the title, however, will face tough opposition from some of the teams yet to come…
__________
That’s all for today folks, I hoped you enjoyed the concept and are looking forward to finding out the level of quality each of the remaining teams have in store. I’ll be posting the next five teams in the next post, so keep an eye out!
Also let me know if i've potentially missed anyone or made any errors.

An Essay on SAT Math

Hi again!
If you haven't seen my posts on Reading and Writing & Language you can check them out here:
https://www.reddit.com/Sat/comments/i17kxs/an_essay_on_sat_reading/
https://www.reddit.com/Sat/comments/i1vfya/an_essay_on_sat_writing_and_language/
Today it's on to Math!
First, I'm not a math guy. I enjoy it as a mental workout (proving the Pythagorean theorem or that there are infinite primes is a fun party trick!) but I hit a wall at calculus in high school and never took a math class in college. I wanted to say that for a couple reasons. I'm not the guy who is going to push a hundred formulas on you for this test. I encourage flexibility and resiliency more than mathiness on the SAT
Also, if I can master this stuff then there's no reason you can't, too!
Okay, so SAT Math.
I think you can really boil down every single math mistake you make into one of three categories:
- A CONTENT MISTAKE - you forgot the circle formula or you never really understood fractional exponents, for instance.
- A FLEXIBILITY MISTAKE - you thought there was one way to do a question and didn't check for other ways to make progress and ended up feeling stuck.
- A PRECISION MISTAKE - you misread the question, or add when you should have multiplied, or even got A for an answer and bubbled in B for some reason.
That's it. Every Math mistake is one of those, or a combination. Now, let me ask you a question, how would you rank those three mistakes in order of most costly to least costly on the SAT? Take a minute, think of what's costing you points on the test. Got an answer in mind? Okay, here's my ordering, based on working with hundreds of kids one-on-one:
PRECISION - 40-50% of mistakes
FLEXIBILITY - 30-40% of mistakes
CONTENT - about 20 percent of mistakes.
Surprised? Most of my students who fear Math think they're going to need to do a lot of content work and that's almost never the case. And very, very close to every student I've ever worked with, whether they come to me with a 500 or a 700 on the Math is making a large percentage of their mistakes on silly errors. DON'T BRUSH OFF THE SILLY MISTAKES YOU MAKE!
HUGE CAVEAT TIME! EVERY STUDENT IS DIFFERENT! The numbers above are a rough starting point, but it's your job to figure out what's really costing you points on this test.
Now, let's talk about fixing these mistakes, starting from the least impactful.
CONTENT MISTAKES
WHAT DO THEY LOOK LIKE - you forgot how to complete the square or what i^3 is
HOW TO FIX THEM - for most students, I recommend fixing content soft spots by practicing PRECISION and FLEXIBILITY and noticing when CONTENT MISTAKES happen. There are also lots of good resources out there that give all the formulas and concepts that might get tested on the SAT. Also, I've had students use Khan Academy successfully for refreshers on content, although I've also had students for whom Khan didn't work well.
PRECISION MISTAKES
WHAT THEY LOOK LIKE - They can look like anything where you have a good plan but execute it poorly: arithmetic mistakes, misread handwriting, being in radian mode instead of degree mode on your calculator. However, the sneaky one is that people often make errors reading the problem. It asks how many horses Bob has and you select the answer for the number of cows, for instance.
HOW TO FIX THEM - Double check everything. This test is completely unforgiving. There's no partial credit and you either bubble in the right response of you don't. Most of my students brush off "silly mistakes" at first, like what matters is that they actually understood the question. THE SAT DOES NOT CARE IF YOU UNDERSTAND THE QUESTION!
HOW TO REALLY FIX THEM - Learn what it actually takes to confirm something. Most times in life we can get away with being 80% sure and if we're wrong someone will correct us. But there's no one there to correct your mistakes on the SAT! So write down what you're doing, draw figures if none are given, double check your arithmetic, reread the question and only do one piece at a time!
Fixing precision mistakes isn't super fun but it's really important if you're going to master this test. One way that I put it is that you're allowed to make as many mistakes as you want as long as you fix them. (Also, I can feel some of you thinking I'm ignoring something important. Don't worry, I see the elephant in the room, too, and I'll go pet it in a minute. Elephants love a good scratch behind the ears.)
FLEXIBILITY MISTAKES
WHAT THEY LOOK LIKE - you get to a hard question, stare at it, and end up guessing blindly because you "didn't know how to do it."
HOW TO FIX THEM - First, realize that the actual mathematics on this test is not super high level. I'm not saying you can't struggle with the math, I'm saying that if you're struggling with a certain question, you need to consider that it might not be the math that's getting you.
Okay, got it? Because that's important. If you believe in your bones that the mathematics is hard, then you'll have trouble really falling in love with the flexibility techniques that pay off on this test.
Next, start training yourself to be flexible. If I was your basketball coach and you were great at right-handed layups but lousy with your left hand I would tell you to spend a week only doing left-handed layups. If I was your piano teacher and you had the bass part down but you were struggling with the melody then I'd make you practice only with your right hand for a week. Don't be afraid to break things down for the SAT and work on individual skills. You can always put it together later.
So here's a flexibility exercise. Print out the Official Practice Test #1 and turn to section three. Do the first ten questions, then go back and do those same ten questions but do each by a different method. Don't worry about getting them right. And definitely don't worry about finding the "right" method. Just see if you can make progress in a completely different way. Because you're working on building flexibility and you can't do that if you're worried about a projected score or the elephant in the room that I'll get to in a minute (I promise.)
Okay, did you do that exercise? Did you notice that on #9 you could solve the simultaneous equations OR you could just plug in the answers? Did you notice on #8 that you could rearrange the algebra OR you could plug in a starting number for "a" and go from there? Did you notice on #10 that in this particular function the absolute value of the input doesn't affect the outcome? Did you notice in #7 that they're trying to intimidate you with a huge equation but nothing can be cancelled so it could never be C or D?
Bottom line, the SAT isn't a math test (or a reading test or a writing test.) It's a decision making test. It's not about finding the right way to make a decision, only a valid way and I promise there are more ways to make progress on Math questions than you probably realize.
The goal when building flexibility is *not to find THE best way to do a question—*although if you get really flexible on this test then you will often see two methods to do a question and be able to tell which might work better—the goal is to never be in a situation where you stare at a question and have no idea how to take a first step. If you really work on flexibility, then you'll always have something you can try.
Here is a checklist I use with students to try to build flexibility. If you ask yourself these three questions on every single math question you do then you will get better a lot faster.

Can I plug in actual numbers (2, 5, 10...) for something in the question that I don't know (usually a variable)? (look at #8 above)
Can I start with the answer choices as a starting point? (look at #9 above)
Can I get rid of any answers for any reason? (look at #7 and #10 above)

There are other cool flexibility things, but those are the big three. Also, getting rid of answers is probably as beneficial as the other two combined and there are so many different ways to find wrong answers on this test. If you get really good at finding wrong answers, you'll almost never find a question without a good opportunity to get rid of an answer for being too big, too small, wrong type (should be an integer or odd or positive or whatever,) or a dozen other little ways. For me, finding reasons to get rid of answers is one of the most fun things about doing the Math.
COUPLE RANDOM WAYS ANSWERS CAN BE WRONG
FIGURES DRAWN TO SCALE ARE A GIFT
All figures on the test are drawn to scale unless indicated otherwise. This almost always means you can get rid of an answer or two simply by estimating the size of an angle or the length of a side.
PANIC ANSWERS
That's my name for the answer you select because you feel panicked and one of the answers just jumps out at you. These are an important subset of wrong answers because not only do students often miss this opportunity, they often gravitate toward panic answers. Panic answers might look like a number in the question, or might be the result of doing one step on the problem, but not all of the steps, or a hundred other reasons. The cool thing about panic answers is that once you get good at spotting them you can eliminate them! The SAT isn't going to—
Holy crap, there's an elephant! Why didn't anyone tell me! (yes, making stupid jokes to teenagers is part of why I do my job.)
Typing everything above I could see a hundred hands go up, feel people squirming in their seats, all over the same question.
"BUT ISN'T THIS GOING TO TAKE FOREVER?!"
To which, I have a simple answer. Yes! And also, No!
I could wax philosophical about SAT timing for a long time, but instead I'll just give you my first rule of timing:

TIMING IS THE LAST THING YOU ARE ALLOWED TO WORRY ABOUT.

I mean that quite literally. After you fix every other soft spot in your process, you're allowed to worry about whether you're timing is working. Imagine learning a difficult piece on the piano. If it's "supposed" to be played at 100 beats per minute that doesn't mean you should practice it at 100 beats per minute always. In fact, if you do that, you'll probably never learn the piece well. Same goes for the SAT.
SLOW DOWN!
MASTER THE TECHNIQUES!
THEN SEE HOW YOUR TIMING IS DOING.
In my experience, if students really master their precision mistakes, their flexibility mistakes, and their content mistakes, then their timing mistakes go away on their own. Always? Nope. Some students do need to really knuckle down and work on timing as a separate issue, but most do not.
A SUMMARY OF MY ADVICE

Forget about timing for now
Get a note card and write down

- Can I plug in actual numbers for a variable (or other unknown)? (Remember #8 above)
- Can I plug in the answers? (remember #9 above)
- Can I eliminate any answer choices? (remember #7 and #10 above, but there are a lot of ways to eliminate on this test.)
If you ask yourself those questions every time the answer will be "no" a lot, but that's okay. Also, it can feel silly to do it on questions that you're already getting right, but it shouldn't! You're practicing to get better at those skills! Don't practice just to get questions right, that's like keeping track of goals in soccer practice.

Review your practice session by looking for missed opportunities to use the three tools on your note card. Also review your practice sessions by asking, for each mistake you made, was it a PRECISION MISTAKE, a FLEXIBILITY MISTAKE, or a CONTENT MISTAKE. The mistakes aren't mutually exclusive, but most students throw too many bad apples in the CONTENT bucket so you should actively try to make yourself care about PRECISION and FLEXIBILITY MISTAKES.

Okay, that's it for now. Any errors are my own, but hopefully this was helpful!

[OC] Predicting the winner of Best Girl 7 using a probabilistic model

This will be a long read, the OP is pretty much me explaining how the prediction model works, if you just want to see the predictions for Best Girl 7 then skip to my first post in the thread.

Updates

(Skip to the introduction if this is your first time reading.)

Update 1 - using a Logit-normal model instead of a Normal model

This is a minor fix in the grand-scheme of things. Instead of assuming the vote share follows a normal distribution we instead assume it follows a Logit-normal distribution. Random variables that follow a L-N distribution have their support bounded to the range [0,1] which means it will never consider impossible probabilities outside of this range which is what was happening before. This is a minor change because the probability of a character receiving a negative vote share or a vote share >100% in the old Normal model was negligible since almost every matchup is in the 10-90% vote share range and the standard deviation is ~5%.
In the calculations below everything stays the same except we are now modelling the Logit of the vote share i.e. we assume logit(V) is a Normally distributed random variable with mean logit(p) and variance σ² (different σ, estimated analytically using past contest data as before: 0.25 in early rounds, 0.30 in later rounds).

Update 2 - improving same-show matchup predictions.

The problem: two characters from the same popular show can dominate opponents in the early rounds and appear to be roughly equal in strength but when they match against eachother one character is the clear favourite and wins by a landslide. This causes the winning character to have an artificially inflated score and results in them being predicted to do better than they should do in later rounds. To explain the fix for this I will use an example.
Example:
In round 4 of Best Girl 5 we had Megumin of Konosuba go up against Wiz, also from Konosuba. Megumin is one of the leads of the show whereas Wiz is a side-character so it's pretty obvious that Megumin should be the favourite here and will probably win by a large margin. Megumin's score going into round 4 was 5080 compared to 2808 for Wiz so the traditional model predicts a vote share of 5080/(5080+2808) = 64.40% for Megumin and a win probability of 99.11%. What actually happened was Megumin won by a scoreline of 12744-2316 and a 84.62% vote share, a full 20% higher and approximately 4 standard deviations away from expected! This would result in Megumin's score rising from 5080 to 6675 making her the overwhelming favourite to win the contest. This is problematic as Megumin likely would not have beaten an opponent from a different show by the same margin so Megumin is rated "too strong" at this point in the contest.
To attempt to fix this (I say attempt because nothing is perfect in statistics) I gathered 39 same-show matchups from seven different contests (Best Character 4, Best Guy 5/6, Best Girl 4,5,6,7) and plotted the expected vote share for the higher seed against the difference in the logit of the actual vote share and the logit of the expected vote share and then centred it on 0.5 (50% vote share). The idea is that two characters from the same show with the exact same score are still expected to have a 50-50 vote share but as the score of one character gets bigger than the other the vote shares become more and more lopsided than what the model predicts. This is what the plot looks like and we can see a general positive trend supporting the idea. A simple linear regression yields a gradient of approximately 5.25 for the line. I should mention that a linear regression may not be perfect since the data does not seem to be perfectly linear. However it is reasonably close to linear as 36/39 (~92%) of the residuals lie within 2 standard errors of the fitted line.
To calculate the new expected vote share for Megumin vs. Wiz we do the following:

Vote Share = logit^-1(5.25 * (0.6440 - 0.5) + logit(0.6440)) = 0.7939

Which means instead of a >20% overperformance Megumin overperformed by just ~5% or roughly 1 standard deviation away from expected, her new win probability is effectively 100.00% (to 5 sf). This was the distribution of the difference in expected vote share from actual vote share before the adjustment (mean = 8.12% overperformance) and this is the distribution after the adjustment (mean = 1.56% overperformance). The mean being slightly above zero shows that it still isn't perfect but it is in line with the distribution of unique-show matchups (1.24% overperformance for the higher seed) which is a good thing as it means characters won't be punished or rewarded for being in a same-show matchup versus other characters in the bracket.
That's all for now, in the future when the dataset of same-show matchups get larger I hope to refine the regression coefficient to be a little more accurate. If more evidence emerges suggesting a linear regression is not suitable I may look into changing the adjustment.

Introduction

For a while now I’ve wondered how one could predict the winner of the contests of anime by using the numbers behind each character. What I would like to produce is a table for each character in the contest with the probabilities of them reaching a certain round such as the finals bracket, winning the whole thing or even just making it to the last 256 for a lower-seeded fan favourite. This would be a bit like what FiveThirtyEight have created for the UEFA Champions League, and ideally one could look at past forecasts to see how well the model forecasted the future.
But how exactly do you assign a probability for one character to receive more votes than their opponent? You could make some complex formula involving the seeds based on previous contest data – indeed statistically the higher seeded character wins around 90% of all matchups, but seeds don’t tell the full story. The seeding of a character is based on the number of votes they receive in the elimination round. In the elimination round voters will vote for any number of characters that they deem worthy of entering the bracket proper. The top 512 get in with the one that received the most votes seeded as #1, the second most voted as #2 etc. Often the top seed isn’t necessarily the most feared character in the contest. Best Guy 6 had Mumen Rider seeded at number 1, yes the side character from One Punch Man outseeded not only the protagonist of the show but a further 510 male characters who were in the running this year! Unsurprisingly Mumen Rider didn’t last as long as his top seed would suggest as he bowed out in Round 4 to 65th seed Jotaro Kujo!
Moreover the actual numbers of the seeds mean nothing in a statistical sense. If seed #1 had 2000 votes, seed #2 had 1500 and seed #10 had 1400 votes in the elimination stage respectively then in terms of raw popularity seed #2 is closer to seed #10 than seed #1 despite the numbers saying otherwise. Thus it is important to consider the elimination votes instead of the seedings.
So it’s clear that while a model must take seedings into account, they aren’t the be-all-end-all of the story and how a character performs against other characters once the main contest gets going is much more important. There are a couple key things you can look out for to identify which characters are overperforming or underperforming their seeds; firstly the vote share which is simply the number of votes a character receives in a matchup divided by the total number of votes for both characters.

E.g. If character 1 beat character 2 by a scoreline of 1500 to 500 then the vote share for character 1 is 1500/(1500+500) = 0.75, or 75% compared to 25% for character 2.

If a character has consistently had a higher vote share in previous rounds than the opponent they are going up against then that signals that there is a good chance they will win the matchup, irrespective of the seeding because they are beating opponents in a more convincing manner. Another key thing to look at is the strength of the opponents faced so far – this is a bit vaguer to explain in words but you can often tell when a character has made it far into the contest by beating bums versus an opponent who has had to knock out several protagonists and pulled off a couple upsets to get where they are.
In summary a good predictive model should take into account three things:

The seeding of the characters, based on the number of votes received in the elimination rounds.
The vote shares achieved in the contest so far.
The strength of the opponents faced so far.

The Model

(There is a little bit of mathematical/statistical knowledge required to understand in this section, you can skip to the example further down if you do not wish to read it and still get a good idea of how the model works.)
I propose the following model, for which we can make predictions from:

For any particular first round matchup M between character 1 and character 2 let X1 and X2 represent the number of votes character 1 and character 2 receive respectively.
Let N := X1+X2 be the total number of votes in M. Define V1 := X1/N and V2 := X2/N to be the vote shares of character 1 and character 2 respectively (note that V1 and V2 are random variables).
Let s1 be the number of votes character 1 received in the elimination round and let s2 be the number of votes character 2 received in the elimination round (note that these values are constants and not random). We shall call these values the score for the characters.
Finally define t := s1+s2 to be the total number of votes for either character in the elimination round and let p1 := s1/t and p2 := s2/t be the proportion of votes for character 1 and character 2 in the elimination round respectively.
Then under this model we make the assumption than V1 and V2 are Normally distributed random variables with means p1 and p2 respectively and have the same variance σ^2.

These assumptions aren’t going to be 100% true for each matchup, to see why note that a voter can vote for both characters in the elimination round so s1 and s2 may contain the same voter whereas X1 and X2 cannot since a person can only vote for one of them in the contest proper. This is exacerbated when two characters from a very popular show that have been dominating opponents meet up in a later round – on paper it looks like it should be close to a 50-50 split but more often than not it is a very one-sided affair because the voter pool is virtually identical for both. The proportions observed in previous rounds are irrelevant because one character may be a more established fan-favourite than the other. In other words the more distinct the voter pool of the two characters is the stronger the assumption that the expected vote share follows the proportions from previous rounds.
The second assumption is that the vote shares follow a normal distribution with identical variance for each character. I will address this assumption later, though do note that empirical evidence suggests that the standard error (used to estimate the standard deviation) is approximately 0.05 in the early rounds and jumps up to 0.10 in round 6 and the finals bracket.

Computing Probabilities with this model

What we would like to predict is the probability that (w.l.o.g.) character 1 receives more votes than character 2 given the observed elimination round votes, that is to find Pr ( X1 > X2 | s1, s2 ). Then by using the model assumptions and the properties of the Normal distribution,

Pr( X1 > X2 | s1, s2 ) = Pr( X1 > X2 | p1, p2 ) = Pr( X1/N > X2/N | p1, p2 ) = Pr( V1 > V2 | p1, p2 ) = Pr( V1 – V2 > 0 | p1, p2 ) = Pr( 2V1 – 1 > 0 | p1, p2 ), since V2 = 1 – V1 = Pr( D > 0 ), where D := 2V1 - 1 ~ Normal(p1-p2, 4σ^2)) = Pr { [D – (p1 – p2)] / 2σ > [0 + (p2 – p1)] / 2σ } = 1 – Φ((p2 – p1)] / 2σ)

Where Φ: ℝ → [0,1] is the Cumulative Distribution Function of a Standard Normal random variable.

Updating the score

So we have found the estimated probabilities that a character wins a particular matchup. Now suppose we observe what actually happened in round 1 and the winners progress to the next round, how do we make predictions for the future rounds? This is done by updating the score to match what we have observed.
Let x1 and x2 be the observed number of votes for characters 1 and 2 respectively and suppose (w.l.o.g.) that character 1 is the winner (so x1 > x2). We compute the observed score, s1*, for character 1 as s1* := t * x1/(x1+x2) and redefine the score of character 1 to be the observed score, that is set s1 <- s1*.
The above process can now be repeated in round 2 and beyond.

Justifying The Normal Assumption

For any particular character we assumed that V ~ Normal( p, σ² ), to test this assumption we can look at a sample distribution of (V – p) which should follow a Normal distribution with zero mean and variance σ² . I looked at data for two different contests: Best Guy 6 and Best Girl 6, both of which took place in the last year and aggregated the differences by round. I wanted to look at four things to test the assumption:

The mean should be approximately zero.
A histogram and a Normal quantile-quantile plot for a visual check to see if the data matches a Normal distribution. The histograms should follow a bell-shape curve and the Q-Q plots should follow a straight line if the data is Normally distributed.
A Shapiro-Wilk test for normality. If the S-W test gives a p-value smaller than 0.05 then there is significant evidence that the data is not normally distributed.
The standard error should be roughly the same in the early rounds and rise in the later rounds as the contest attracts more attention, introducing newer voters and making the finals bracket more volatile.

Sample distribution of (V-p) in Best Guy 6 by round
On visual inspection it seems that the data does follow a Normal distribution in each round and the S-W test agrees with this conclusion with the exception of round 2 when there was a big outlier in the matchup between Ainz ooal Gown and Cocytus. Based on the scores for both characters Ainz was expected to win with a vote share of ~65% but instead won with a massive 88% share for a difference of 23%. This is the downside of the model I was speaking about earlier, since both Ainz and Cocytus are in the same show the pool of voters voting for both characters is virtually identical and so we cannot make the normal assumption for this matchup. If you remove this matchup from the data then the S-W test gives a non-significant p-value of 0.3122.
Sample distribution of (V-p) in Best Girl 6 by round
Similarly the data from Best Girl 6 also seems to follow the Normal distribution with the exception of Round 1 which saw a massive upset between 406th seed Himari Takanashi and 107th seed Yui. This upset seems to be some form of SAO spite-voting (which is funny since Asuna would go on to win the contest) and highlights a second flaw of the model in that it can’t really predict spite-voters or strategic voters since they represent a different population to those that have voted in a characters’ matchups so far. Removing this outlier gives a non-significant S-W test p-value of 0.07224.
In both contests we see that the standard error stays relatively constant at around 0.05 until you reach round 6 (last 16) when it seems to double to around 0.10. The model will incorporate this by having the standard deviation be 0.05 until round 6 when it will change to 0.10. One reason for this increase in variance could be the large jump in people voting in later rounds as the contest gets bigger exposure. Finally the means for each round are slightly above zero suggesting that characters with higher scores (usually higher seeds) typically overperform relative to their expected vote share, this is because the differences for each round are taken with respect to the higher seed. There are a number of possible reasons for this, one being that the population of voters who have seen both characters may aggressively favour the higher seed over the lower seed, skewing their result. Still the mean is close enough to zero that the assumption seems valid.

Example

That all might seem like a lot to take in so I think an example will make things clearer. Let’s suppose we’re in a simple 4-girl contest and the matchups are Holo vs. Megumin and Kaguya vs. Mai with the winners facing off in the final. Each girl received the following number of votes in the elimination round to determine their seedings #1-#4:

Seed	Girl	Elimination Round Votes (score)
1	Kaguya	2600
2	Megumin	2400
3	Holo	2400
4	Mai	2400

By just eyeballing the numbers you can tell that Kaguya should be the favourite over Mai while Megumin and Holo should each have a 50% chance of advancing but what does the model say?
Kaguya vs. Mai
Consider Kaguya as character 1 and Mai as character 2 then p1 = 2600/(2600+2400) = 0.52 and p2 = 2400/(2600+2400) = 0.48. These are not the probabilities for each character to advance to the next round but instead are the expected vote shares for each character (52% for Kaguya and 48% for Mai). To find the probabilities that either character advances we use the equation derived above based on the model,

Pr(Kaguya wins) = 1 - Φ((0.48 – 0.52)/(2 * 0.1)) = 1 – Φ(-0.2) ≈ 0.579.

Which implies

Pr(Mai wins) ≈ 0.421.

So Kaguya is the clear favourite and is expected to win around 58% of the time. Now suppose the actual results come in and big shock! Kaguya loses by a scoreline of 4500-5500, or a 45-55 voter share ratio. Since Mai has won and moved on to the next round we need to update her score, her new score is the value her score should have been to minimize the difference which is

Mai's new score = (2400 + 2600) * 0.55 = 2750. Kaguya's new score = (2600 + 2400) * 0.45 = 2250.

Note that Kaguya’s observed score falls down to 2250 so that their new scores perfectly reflect the 55-45 observed ratio.
Holo vs. Megumin
With the same setup as above we have that p1=0.50 and p2=0.50 and the probability that Holo advances to the next round is:

Pr(Holo wins) = 1 - Φ((0.50 – 0.50) / (2 * 0.1)) = 1 – Φ(0) = 0.500 => Pr(Megumin wins) = 0.500.

So there is a 50% chance that Holo wins and a 50% chance that Megumin wins. Now suppose the results come in and in classic anime fashion Holo also wins by a scoreline of 5500-4500. Note that based on the seeds this would be classed as a big upset since Holo is seeded lower than Megumin but in reality because their votes in the elimination round were identical it isn’t. Holo’s updated score is

Holo's new score = (2400 + 2400) * 0.55 = 2640. Megumin's new score = (2400 + 2400) * 0.45 = 2160.

and we move on to the final!
Mai vs. Holo
Going into the final Mai (2750) has a higher score than Holo (2640) despite winning by the same victory margin in the previous round. This is because Mai defeated a stronger opponent than Holo did, which was the third thing we wanted our model to incorporate. With the same setup as above we have that p1 = 2750/5390 ≈ 0.51 and p2 = 2640/5390 ≈ 0.49 and so

Pr(Mai wins) = 1 - Φ((0.49 – 0.51) / (2 * 0.1)) = 1 – Φ(-0.1) ≈ 0.540.

Which implies

Pr(Holo wins) ≈ 0.460.

So we expect Mai to win the final against Holo approximately 54% of the time. This is nice to compute but we had to wait and see who would be in the final to find out what their chances of winning the contest was, how can we find out the probability that one of the girls would win the whole thing back in round 1? Let’s use Holo as an example.
Finding Holo’s chances of winning in round 1
The probability Holo wins the contest is the same as the probability of Holo reaching the final multiplied by the probability Holo wins in the final conditioned on her getting there. We already computed the first probability to be 0.500 and by the Law of total probability the second probability is

Pr(Holo wins the final | Holo reaches final) = Pr(Holo beats Mai) * Pr(Mai reaches final) + Pr(Holo beats Kaguya) * Pr(Kaguya reaches final) = (0.50 * 0.421) + (0.421 * 0.579) ≈ 0.454

since her only possible opponents are Mai or Kaguya and we don’t yet know which one will reach the final. Thus the probability Holo wins the contest when all four girls are remaining is 0.500 * 0.454 ≈ 0.227. Note that this is not exactly one in four because Kaguya’s high score weighs the chances more in her favour. If we compute the probabilities for the other three girls we find that:

Girl	Win prob in round 1	Percentage
Kaguya	0.335	33.5%
Megumin	0.227	22.7%
Holo	0.227	22.7%
Mai	0.211	21.1%

So you would expect Kaguya to be a big favourite to win the whole thing out of the four, but more often than not someone other than her will win.

Generalising to bigger contests

If you’re savvy to how the above computations work, you’ll notice that as the number of rounds in the knockout contest increases (resulting in the number of participants increasing by a factor of 2 raised to the power of the number of rounds) the number of computations required to compute the overall win probabilities drastically increases. Finding the win probabilities of a 512-man contest in round 1 can only ever be done by a computer and so that’s what I set out to do. You can find my script (written in R) used to generate the output files in a folder in the Outputs section. I won't claim it’s optimised, indeed forecasting the winner from round 1 takes several minutes to compute on my old laptop but it gets the job done and later rounds fly by almost instantly. If you want a fun challenge try and write a script that computes the probabilities in a faster than exponential order of time.

Outputs

Below is a download link to a folder containing the probability forecasts for the recent Best Girl 6 and Best Guy 6 contests, which I used as a template to write my script. The script is also included along with a readme file to help you recreate the outputs. Please let me know if the link is broken!

Google Drive download link

Best Girl 6

Megumin was the clear favourite going into round 1 as she boasted a massive 3028 adjusted votes in the elimination rounds, which was significantly higher than second seed Aqua (2880) and third seed Holo (2663). This is reflected in the pre-contest probabilities as Megumin was given a 36% chance to win compared to 25% for Aqua and 15% for Holo. This probability increased further in round 2 after she won her round 1 matchup with a 91% vote share – the highest of the entire contest.
Megumin remained the strong favourite until round 4 at which point cracks began to show in her dominance – she was still doing well but so was Holo, who also had an easier ride to the finals as Mikasa, Mayuri and Saber were all still alive on Megumin’s side of the bracket. By the end of round 5 Holo took the lead as Jibril and Hachikuji had suddenly emerged as strong candidates in Megumin's half of the bracket. Mayuri was no longer looking like a pushover for Megumin and indeed Megumin would bow out in arguably the biggest upset of the contest to her in the next round leaving Holo as the clear favourite… Or so you would think, but Holo herself had a relatively poor round 6 as well, defeating the weak Yunyun by a smaller margin than expecting whilst Aqua and Mikasa posted dominant victories against tougher opposition. Mikasa would crush Mayuri in the quarter-finals to become the new favourite after Holo bowed out in a very surprising loss to Winry.
Also flying under the radar this whole time was Yuuki Asuna who in round 5 had under a 1% chance to win the title. Her stock had risen though after knocking out the dangerous Jibril in convincing fashion in round 6. With Mikasa as her quarter-final opponent she was given a 32% chance of winning, but she defied the odds and won in a dominant fashion to set up an unlikely final with Winry, who similarly defeated Aqua in equally convincing style!
The estimated probability that Asuna would make the final was 17% in the pre-contest and only 9% for Winry, at this point Asuna was deemed the favourite by the model, and was given a 59% chance of defeating Winry. The predicted vote share was 52-48 in Asuna’s favourite which she demolished by taking home the sixth crown with a whopping 63% of the vote!
Model Accuracy in Best Girl 6
Overall the model correctly favoured the winner in 466/511 matchups (91.2%) which was higher than the 460/511 matchups (90.0%) matchups won by the higher seed, suggesting evidence that the model predicts as good or better than just predicting the higher seed to advance. The success rate by round is broken down below:

Round	Correct Predictions (Model)	Correct Predictions (Seeds)
1	235/256 (91.8%)	234/256 (91.4%)
2	122/128 (95.3%)	120/128 (93.8%)
3	57/64 (89.1%)	55/64 (85.9%)
4	29/32 (90.7%)	27/32 (84.4%)
5	14/16 (87.5%)	14/16 (87.5%)
6	5/8 (62.5%)	5/8 (62.5%)
Finals	4/7 (57.1%)	5/7 (71.4%)
Overall	466/511 (91.2%)	460/511 (90.0%)

Best Guy 6

Best Guy 6 was a much more different affair to Best Girl 6 in that the elimination round votes for the top seeds were a lot closer together. This is reflected in the probabilities as seven characters were given a 5% probability or greater of winning the whole thing in the pre-contest (as opposed to four in Best Girl 6). Note that the number one seed, Mumen Rider is quickly identified as being seeded too high and is actually considered the underdog in his round 4 matchup against 65th seed Jotaro Kujo, who he lost to.
I remember in the early rounds the perceived “big three” were Reigen Arataka, Satou Kazuma and Edward Elric and indeed after round 2 these were the three favorites according to the model, though Shirogane Miyuki and Levi Ackerman were also identified as strong candidates.
Kazuma became the outright favourite next after crushing his round 3 opponent with a 86% vote share – a dominant showing that none of the other favourites could reply to. Second-favourite Edward Elric went a bit off the boil in rounds 4 and 5 – he still won handily but not by enough to keep pace with Reigen and Kazuma who shared the title of favourite for those rounds.
Everything changed in round 6 though – Kazuma survived a scare against Killua Zoldyck, winning by just a single vote whilst Reigen saw opponents in his half of the bracket grow stronger. Levi became the second favourite at this point whilst Edward Elric emerged as the most likely character to win after crushing Alphonse, though admittedly his stock may have rose a little too high since Alphonse is from the same show after all.
Levi proved his superiority over Reigen in the quarter-finals as he beat him by a margin pretty similar to what the model predicted. Interestingly Saitama beating Kazuma wasn’t so out of left field as I thought at the time; according to the data he had a 45% chance of making it to the semi-finals. It was in the semi-finals that one of the biggest upset of the contest occurred when Saitama defeated Edward Elric to book his place in the final against Levi who at this point was crushing opponents left and right. Saitama was given only a one in five shot of beating the titan-killing prodigy and he did not take it as Levi won by an even more comfortable margin than he was already predicted.
Model Accuracy in Best Guy 6
For Best Guy 6 the model correctly favoured the winner in 470/511 matchups (92.0%) which was higher than the 460/511 matchups (90.0%) matchups won by the higher seed, suggesting further evidence that the model predicts as good or better than just predicting the higher seed to advance. The success rate by round is broken down below:

Round	Correct Predictions (Model)	Correct Predictions (Seeds)
1	241/256 (94.1%)	241/256 (94.1%)
2	121/128 (94.5%)	118/128 (92.2%)
3	57/64 (89.1%)	56/64 (87.5%)
4	26/32 (81.3%)	23/32 (71.9%)
5	14/16 (87.5%)	12/16 (75.0%)
6	7/8 (87.5%)	6/8 (75.0%)
Finals	5/8 (62.5%)	5/8 (62.5%)
Overall	470/511 (92.0%)	460/511 (90.0%)

In summary over the two sample contests the model correctly favoured the winner in 936/1022 matchups compared to 920/1022 if you used a simple model that just favoured the higher seeds. This corresponds to an error rate of 8.4% for the Normal model versus an error rate (AKA the upset rate) of 10.0% for the simple model.

Final Words

The Normal Model seems to achieve the three things we set out to do and based on data from recent contests while also having good predictive power. With that said there are some improvements and adjustments that could be made to make it even better. The first thing would be to deal differently with matchups between characters from the same show; these are normally one-sided and can result in artificially inflated score values for the winner. A good example of this was Ainz ooal Gown’s dominant win over Cocytus in round 2 of Best Guy 6 giving him a much higher score than he should have had at that stage. He would lose to Gilgamesh (who was higher seeded) in the next round despite being predicted to be the strong favourite because of this higher score, whilst simultaneously passing on some of the inflated score points to Gilgamesh, creating a knock-on effect. One solution would be to freeze the scores for characters in same-show matchups. Secondly you could experiment with the value of the standard deviation and possibly vary it depending on the seed of the character. You could also introduce a weighting parameter to the score updating function so that earlier rounds are weighted a little heavier than they currently are. In the end I decided to stick with the vanilla model because the simplest is usually the best (and I didn’t fancy testing stuff for another couple days haha!)
I hope you found this to be an interesting read; I will be posting the updated probability forecasts for each girl every day in the Best Girl 7 contest threads along with the daily results post. If you have any feedback on the model please let me know, this was a very fun project to take on!

soccer odds value calculator video

-True odds of 1.39 (value calculator probability of 71.75%). Market odds of 1.72 (Betfair) *Sporting Kansas City vs Vancouver Whitecaps – Under 3.5 Goals (back)-True odds of 1.29 (value calculator probability of 77.67%). Market odds of 1.44 (Betfair) Is this inconsistent? Am I doing something very wrong? The calculator to the left can be used to remove vig from 3-way markets such as 1X2 used in football (soccer) and sometimes hockey. In other words it can be used to handicap the market price for sports where there is the option to bet on Home, Visitor, or Draw. This calculator supports both European decimal odds and American moneyline odds. Poisson Distribution Calculator. This simple Poisson calculator tool takes the goal expectancy for the home and away teams in a particular match then using a Poisson function calculates the percentage chance and likely number of goals each team will score. Value Calculator: Work Out Bet Expectation. One of the key concepts you need to understand as a punter is the relative value of a bet. You can now judge the value of any bet, by using this Value Calculator, which takes a single bet and its probability of success and then calculates the likelihood of that bet turning you a profit (or loss) if repeated over 100 times. The greater the profit Using our odds value calculator, you can get the expected value of your bets by comparing your projections to the odds listed by sportsbooks. Expected Value Definition Expected value is the amount of money you can expect to win or lose if you placed the same bet on the same event at the same probability an infinite number of times. The Football Odds Calculator is a free tool to estimate fair odds for soccer bets. Method of calculation: Mathematical football predictions are based on 1x2 odds. We analyse 1x2 closing odds from the past and can predict expected and fair odds for any real or virtual soccer game. If we then take a look at the odds from a well known bookmaker we see they have priced the game up as follows; Match Odds: Fulham 2.20 Draw 3.30 Aston Villa 3.40 Fulham are 2.20 (45.45%) and we Value bets tool help you explore mathematically calculated selections where the probability of the outcome is greater than the odds reflect. We increase your chances to win and to be profitable in the long run by revealing advantageous odds at different bookmakers. Your source of free betting tips, free football predictions, free odds comparison and match previews.BetClan Value Bet Calculator - Find Value Bets Calculate expected value in betting, Using the odds value calculator allows you to see the value which you would expect to receive for a bet based on the probability of that outcome occurring. , key features and key question way to predict soccer

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Probability Calculation Football Betting Tips explained ...

Subscribe Now:http://www.youtube.com/subscription_center?add_user=EhowtechWatch More:http://www.youtube.com/EhowtechUsing Excel to calculate probabilities is... How to find probability and odds and the difference between the two. We also discuss experimental probablility, theoretical probability, odds in favor, and ... http://www.soccerwidow.com/betting-maths/true-odds-value-detector-league-games-h2h-history/'VALUE' bet detection: The spreadsheet calculates true odds (expec... About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators ... Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! In this video, I show the ... Need to create a league table in an Excel spreadsheet? This series of 3 videos breaks down the key steps.Download file link:https://tinyurl.com/yyuvpc7eVid ... With the future bet soccer prediction model, you have to chance to correctly predict correct scores and even value bet with ease. http://www.soccerwidow.com/betting-maths/true-odds-value-detector-international-club-competitions-with-no-head-to-head-history/True Odds & Value Bet Detector... What I use: I draw my pictures on Paint Tools Sai and I edit it all in Adobe Premiere. (Really? You don't use any animation software??) (Yeah, I know it's ''''not real animation''''') I have a ... Algorithm probability calculation football betting tips is useful tool for every player who place bets in bookies because helps to predict correctly results ...

Odds Value Calculator | The Action Network

soccer odds value calculator

soccer odds value calculator - win

If you ask the question “why is team A at this odds?” every week...

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Quotes

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[OC] Predicting the winner of Best Girl 7 using a probabilistic model

Updates

Introduction

The Model

Computing Probabilities with this model

Updating the score

Justifying The Normal Assumption

Example

Generalising to bigger contests

Outputs

Google Drive download link

Best Girl 6

Best Guy 6

Final Words

soccer odds value calculator video

soccer odds value calculator top

Probability Calculation Football Betting Tips explained ...