Tag Archives: yaya toure

EPL Player Ratings, Games 1-6

6 games into the season, the most influential players for each team are elbowing their way up the charts to prominence.

As in the previous post covering games 1-3, I have reproduced the same report format to provide information on every team. In subsequent posts my intention is to produce these reports for discrete periods, e.g. games 1-6, then games 7-12,…etc to see if trends in these reports can give insight into changes in player form/influence or team tactics.

First up, I calculated a summary report of all players who have played over 270mins (~3 games) so far i.e. no Ozil who is on 248mins.

All 1-6Sigurdsson, Ramsey and Yaya have all received praise for their performances at the start of the season and these measures support that (scoring goals certainly helps). Pienaar, Fellaini and Ward have all performed notably as well. Vidic has the highest defending/ball-winning score whilst hamstrung Pienaar leads the way in passing/retaining the ball.


AFC 1-6Unsurprisingly, Ramsey and Ozil lead the charts for Arsenal – although Gnabry takes 3rd place in terms of average rating. Few would doubt Flamini’s contributions so far this year off the ball, but he is a fair distance down the table by these measures due to their bias towards ball-related actions.

Aston Villa:

AVFC 1-6Injured Benteke and Okore top the charts for Villa, but they have still managed to win their previous 2 games without their influence.

Cardiff City:

CCFC 1-6The wonderfully named Théophile-Catherine has had a bright start to life at Cardiff.


CFC 1-6Terry, Hazard and Ivanovic stand out here, although despite playing relatively few minutes Mata has the highest involvement rating. This may be indicative of how important he is to Chelsea as a link between defence and attack.

Crystal Palace:

CPFC 1-6Ignoring 12 minute O’Keefe, defenders Ward and Jedinak have been Crystal Palace’s key players so far. J Williams deserves a mention for the highest non-O’Keefe attacker rating.


EVE 1-6Full-backs Baines and Coleman very prominent for Everton.


FFC 1-6Fulham’s team stats have been much derided in the early stages of the season and player-wise there isn’t a lot to shout about either. They must be hoping for better from Hangeland, Berbatov and Ruiz as the season progresses.

Hull City:

HCFC 1-6An improvement from Curtis Davies in the last 3 games takes him to the top of Hull’s table.


LFC 1-61 great performance from Suarez and he’s top. Sakho has impressed early on with his defending stats.

Manchester City:

MCFC 1-6Silva and Nasri are vying for most involved player, whilst Navas has had a very lukewarm start to life in Manchester.

Manchester United:

MUFC 1-6Some very strong scores at the top end of the chart for Man Utd – Evans, Smalling and Nani all making good cases for more game time. Carrick seems to be slightly less involved than observed last season as Fellaini has quickly become the main throughfare in midfield.

Newcastle United:

NUFC 1-6Mixed scores with Santon continuing to impress, Remy coming to the fore and Tiote heavily involved.

Norwich City:

NCFC 1-6Nowhere near as much influence this season from Snodgrass as Redmond and Fer have become important attacking midfielders for Norwich – interestingly the opposite end of the table suggests van Wolfswinkel isn’t offering the team a great deal so far!


South 1-6Only 2 goals conceded by Southampton so far, owing much to Lovren and Fonte’s impressive pairing at the back.

Stoke City:

SCFC 1-6Keep-it-simple N’Zonzi and creator Charlie Adam catch the eye due to involvement and attacking attributes respectively.


SAFC 1-6Cuellar achieved a score of 10 for defending and ball-winning in his only appearance of the season against Liverpool, but it obviously wasn’t enough to prevent Sunderland losing 1-3.

Swansea City:

Swans 1-6Ben Davies’ marauding runs from left back and 2 goals lead him to the top of the Swansea player table.

Tottenham Hotspur:

THFC 1-6Sigurdsson, Townsend and Walker lead the way. Eriksen scores highly in passing and involvement but according to the stats he hasn’t quite done enough to lift him above many of his teammates at this early stage.

West Brom:

WBA 1-6Amalfitano and Sessegnon have begun the season quite well as defenders Ridgewell, McAuley and Olsson have earned 1 man-of-the-match award apiece.

West Ham United:

WHFC 1-6Difficult start to the season for West Ham, as they look very short in attack. J Cole and Morrison look to be their best outlets in forward positions until they can patch up Carroll.


Model pitfalls and further discussion of TPOEM

Since my previous post introducing a new model for football analysis, TPOEM, I have developed and integrated some significant improvements to it.

Firstly the speed in which I can give predictions based on team starting line-up (involving less manual input, more automation) is much better, so last Saturday I was able to tweet about the model’s predictions well before the 3pm kick-offs began.

Secondly I have added a manager/leadership factor into the analysis which is dynamic and unique to each team.  This adjustment is intended to ‘smooth’ the team level aggregate scores that TPOEM calculates, where the model would not otherwise capture a persistent difference between a team’s results and their underying scores. This offsets (albeit not completely) the difference between the model’s league table compared to the actual league table. Why does that happen? Well, the basic underlying reason is the same as why a shots on goal league table does not reflect the real league table. I attribute this to a kind of quality factor that I am not picking up in the statistics I use: quality in terms of shooting can relate to the position on the pitch of a shot, whether defenders pressured the attacker and how much of a contribution the assist added to a goal scored. This quality factor will also incorporate a team’s record at home or away. For reference, the model currently seems to think that Stoke and Norwich are outperforming particularly well whilst Wigan, Southampton and QPR are all doing worse in the league than TPOEM suggests they should be doing. That might be due to luck, team playing style, management, player leadership, quality or all of the above. The model should now be slightly better at accounting for that.

Predicting part 2

So the first week of predicting using TPOEM brought me a net proft, although my biggest win was West Ham away win vs Stoke – and I’ve already explained that the model was distinctly anti-Stoke before the most recent update!

Again, as ever, I am seeking value so even if TPOEM suggests a probability of an event win/draw/loss of about 40%, if the bookmakers quote odds of 35% then I consider it an attractive bet. As it stands I haven’t been that selective about what I bet on: in fact so far I’ve been betting on every match that I ran the model for even though in many cases the model didn’t really suggest any particular value vs bookies.

The result this week, from 5 games, was another net profit, this time +26% return (it was +56% last time). But that came from 2 wins, 1 void, 2 lost bets, so in a sense the net result was neutral.  I profited overall because I weighted my bets towards the most attractive in terms of value – the biggest win being a draw-no-bet backing Everton at home to Man City. The model really liked Everton’s chances mostly because Kompany, Aguero and Yaya Touré were all missing for Man City.

I also backed draw-no-bets for Liverpool, Villa and Stoke: lost, won, void respectively. And lastly I went with a draw for Swansea-Arsenal (lost) but in retrospect I shouldn’t have bothered with that bet because the model gave no conclusive direction for the game and the odds weren’t good either.

As I reformat the model’s data and find a better way of communicating its predictions/results I will publish more information on the blog as I recognise I have kept most of the details pretty close to home so far. When I’m at my desk for the 3pm kick-offs I will also tweet about the model’s predictions so if you’re interested look out for that but if you bet then you are doing so at your own risk!!!

Premier League 2011-12: Position Analysis CM

Reviewing Opta statistics for goalkeepers, central defenders and full backs was easier in terms of how to classify each position because as I explained in my first post on formations, almost every team lined up with a back four last season (on only 21 occasions out of 760 did a team not start with a back four).

Now that we are looking at midfielders, although four ‘flat’ midfielders was also the most common line-up, in general there was a lot more variety. A defensive midfielder should not really be compared with a winger, so I have had to be more careful when it comes to classifying the position that a midfielder plays depending on his position in the team’s formation. As we will see later, by pigeon-holing players in this way the review may be flawed for some players who qualify for the CM category but in reality are more defensive than average.

For transparency, I have listed my classifications below:

As we can see from the above list of CMs, which is fragmented by the formations, it was more of a challenge to remove the players who did not play in this position from the initial correlation review – due to the less consistent position ids. I also removed players with fewer than 1000mins in the position of CM in order to maintain consistency with my previous reviews .

I found that offensive statistics for CMs dominate defensive statistics. For example, goals, assists, through balls and big chances were all slightly more important to picking up points than the most significant defensive fields such as headed clearances and ground duels. This seems to suggest that the all-action central midfielder is more important in attack than defence. As a result my rating has 5 offensive statistics which rate the 48 midfielders with points from 1-6 (worst to best), 1 neutral field (pass success rate, middle third), and 3 key defensive statistics each with a maximum of 6pts on offer for the best 8 midfielders in each category. Errors leading to goals and red cards were so infrequent that it seemed unfair to apportion too much significance to them to I simply deducted a point per occurance for each player.

The final table is below. Note the absence of any Swansea or Arsenal players from the list: this is because neither team set up with a formation including a CM as per my definition – both Rodgers and Wenger prefer to employ the 4-2-3-1 formation.

The winner is another Manchester City player: Yaya Touré. Touré was without doubt instrumental to City’s success last season and as we can see from the rating he was excellent in all the categories that matter in my chart. I am starting to wonder if there is an inherent bias in the data MCFC have made available to the public considering how many categories City are dominating! The reality however is that the best players in the best teams should generally stand out in this type of analysis. Arguably Touré’s best statistic is middle third passing success, in which he was 2nd best overall. On average he completed 17.1 passes in the middle third before one was misplaced which compares to an overall average of 7.5 (successful passes:unsuccessful passes). However the winner in that category was the influential Paul Scholes, who on average completed a colossal 23.3 passes before one was misplaced!

Notable players in the top 10 include Stiliyan Petrov (whose season was sadly cut short due to his diagnosis with leukaemia) and Wes Hoolahan, both of whom were key midfielders who played for bottom half teams. Petrov was strong in most fields, but had a particularly low rate of being dispossessed, whilst Hoolahan was very strong in offensive categories – with similar attacking qualities to Frank Lampard.

Steven N’Zonzi, who was the player from a relegated club with the highest position, rated =14th in the list despite being considered by many to be a defensive midfielder – a significant result considering the general lack of more defensive-oriented CMs in the top half of the list.

For the second post running a Newcastle player finished last – surely not?! Rated by many to be one of Newcastle’s outstanding players, Cheick Tioté’s qualities are clearly not suited to this system. As we might expect, Tioté’s offensive statistics were poor: he didn’t score, he made one assist and played one through ball all season. What is perhaps more surprising is his below average middle third passing success ratio (6.5), the fact that he lost more ground duels than he won (169 to 122) and he was dispossessed more times than any other CM (69 in total).

And another surprise in the form of Luka Modric, who might be assumed to perform well in this model but only rated 33rd. These players deserve better, and so at a later date I will look to ascertain the qualities that set them apart from the crowd.