Tag Archives: Opta

TPOEM EPL Player Awards 2012/13

I have stripped out players who played less than 500mins and provided some top/bottom 10s based on the TPOEM player ratings model I created earlier this year – calculated from matches in the Premier League only.

It’s a little biased, in more ways than one. The key bias I want to highlight before I start is that the stats for players in ‘busy’ teams are biased upwards – Liverpool and Spurs in particular were relatively active last season. When I say ‘active’ or ‘busy’ I mean that they exhibited high frequencies in certain actions – shots, tackles, ground duels, dribbles, etc. which improved the TPOEM ratings for a team’s players without necessarily providing the gains expected from the average team in terms of goals scored and points won. Hence TPOEM, and many other models to rate players by stats only, ought to be viewed a little critically – i.e. TPOEM suggests that Suarez was the best forward in the league last season, this is due in part to the fact that he attempted a very high proportion of ground duels, dribbles and shots. Calling him the ‘best’ forward is arguable – but there is little question about his results from this particular selection of binary stats.

I will highlight the key metrics I defined earlier in the season, and which players were in the top and bottom 10 for each category. Plus man of the match awards and even tallest/shortest players.

Ball Winning & Defending – this measure depends on things like clearances, tackles, interceptions and how successful the player was at completing each of those actions. Unsurprisingly the worst rated players are mostly attackers, but there are a couple of surprises in the ‘best’ list.

Ball winning & defending TPOEMPassing & Ball Retention – this measure compares how often a player lost the ball in relation to his success rate of passing in different areas of the pitch. You can see the bias towards the top teams who retained the ball very well in the attacking areas.

Passing & ball retention - TPOEMAttacking – goals, shots, dribbles, chances created and a few others in this one. Sturridge did brilliantly after joining Liverpool (Ba’s rating in the table below represent his results at Newcastle only).

Attacking - TPOEMDiscipline – this is effectively a measure of how often a player received yellow/red cards, plus fouling the opposition, minus the fouls he won. The best players are the ‘nice guys’ who seem to get fouled a lot without giving much back. The worst are those with a short temper who flirt with the possibility of a sending off too much for their own good.

Discipline - TPOEMInvolvement – this measures how often a player is involved in various actions for his team, be it passing, tackling, duelling, shooting and so on. The players rated highest are all midfielders, whilst those rated ‘worst’ are generally central defenders/strikers who generally don’t touch the ball that often.

Involvement - TPOEMMan of the Match Awards – the team awards are simply the highest rated player on the team for a given match whilst the overall award is of course the ‘global’ dominant player in a match, for either team. Taarabt and Snodgrass get notable mentions here but Cazorla is the real winner.

Man of the Match - TPOEMBest Total Contribution – dominated by attackers, as TPOEM is biased towards goalscoring / creating.

Best Total Contribution - TPOEMBest Players By Position – I have classified players into different positions and shown the best performers below:

Full backs - TPOEM Central Defenders - TPOEMDefensive Mids - TPOEMMidfielders - TPOEMAttacking Mids - TPOEMForwards - TPOEM

 

Tallest & Shortest Outfielders – as published on whoscored.com. Players over 500mins only.

Tallest & Shortest - TPOEM

 

Advertisements

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!!!

Introducing TPOEM

I must say I sometimes get irritated by the overuse of acronyms in today’s world but this time I’ve created my own. TPOEM rather unimaginitively stands for The Power Of Eleven Model which I have been developing over the past few weeks.

TPOEM is the culmination of fairly light research into simple OPTA-derived football statistics that I have been analysing over the past 6 months or so. Having only really put the information together over the past week or so, it is a bit foolhardy to discuss TPOEM in any detail right now – but I have already begun using it to objectively rate player/team performance and even test its efficacy at predicting match results.

I will give some detail into how the model works. The first point of note is that it is a bottom-up system.  That means that it primarily analyses player data first and team data second. There are many reasons I wanted to approach the analysis in this way:

  • A focus on player statistics gives an objective view of a player’s importance to a team, and can help indicate which players contributed most/least to a team’s performance
  • Player statistics like goals scored and assists are readily available and easily compared between players at different clubs
  • TPOEM can potentially capture information that is useful to understanding team playing styles
  • TPOEM can potentially be used to give a prediction of a match result based on the team starting line-ups, which will give a clearer expectation of a result if key players from either team are missing

Although TPOEM is derived from fairly simple statistics, the most recent iteration incorporates 36 statistics including stats from goals scored and shots on target to tackles and ground duels. I have weighted the utility of each action and applied success rates where available to give a rating in simplified categories:

  • Defending/Ball winning
  • Passing/Ball retention
  • Attacking
  • Discipline
  • Involvement
  • Goalkeeping

Of course the overall scores are adjusted so that the most frequent actions (passing, touches, etc) do not grossly outweigh the less frequent, but arguably more important, actions such as shots on target and goals scored. At the same time, I tried to maintain some care over the relevance of goals as a statistic – of course goals win games, but why should TPOEM rate attackers more highly than defenders because they score more often? Strikers often take all the plaudits for scoring goals but since most goals are scored inside the box I have tried not to unduly credit a goal scored – in many instances it is easier to score a goal than miss. I took a similar view of assists, seeking not to overly ramp-up a player’s score simply because he completed a pass (however important it was). I have to stress that it still wasn’t quite a finger in the air approach to rating – I have reviewed correlations to team performance at various layers with the aim of giving my weightings a scientific basis.

I have now tinkered with the algorithms enough times to realise that although TPOEM in one sense gives an objective rating of player performance, but in another sense remains a reflection of its creator’s biases and research. This is limitation of any model, which can only be improved by testing and further research.

What about results? Well I will keep publishing information over the coming weeks as I look to find suitable ways of presenting TPOEM’s output.

For now, I have run the model on the first 271 games of the premier league season (i.e. before the kick-offs on the 2 March), and I can announce its candidates for the most man of the match performances so far this season:

Player MoM awards
Santiago Cazorla 13
Gareth Bale 10
Adel Taarabt 8
Eden Hazard 8
Leighton Baines 7
Luis Suárez 7
David Silva 6
Dimitar Berbatov 6
Juan Mata 6
Marouane Fellaini 6

This highlights the importance, according to TPOEM, of Santiago Cazorla to Arsenal’s season in terms of match-winning performances. Both Manchester sides and Arsenal lead the team man of the match awards with 22 apiece, the difference being that there is a much larger spread of players who have put in top performances for United and City in the league.

Predicting

Those readers who follow me on twitter will have noticed that TPOEM liked the value of the chances of a home win for Everton and draws for Swansea vs Newcastle and Manchester United vs Norwich. Please note that this isn’t a direct match result prediction for the above – TPOEM actually had all 3 as odds-on for home wins, but the probability of a draw when compared to quoted bookmakers odds before 3pm seemed attractive at the time.

The main problem I had was in finding an efficient way to input all the line-ups in time for kick-off!

As it was, I completed my efforts and placed bets on all the 3pm kick-offs by 3.25pm – something I will have to work on going forward.

In addition to the above bets, of which only Everton’s home win against Reading paid off, I bet on a draw for Sunderland-Fulham (profit) an away win for West Ham (profit) and a win for QPR. 2 of these bets were actually placed live, with the scores at 0-0, whilst QPR were already 1-0 up at Southampton when I took the gamble of backing them to win. According to TPOEM, Chelsea were massive favourites at home to West Brom so I decided not to bother with a gamble on that game.

Most pleasing was the away win of West Ham at Stoke – a game which I am sure could just as easily have gone either way. When I ran the line-ups through TPOEM West Ham had actually already made 2 early substutions so I incorporated those new players into the line-up. The model indicated about a 30% chance of West Ham winning which was attractive enough when compared to quoted odds of about 9/4. Fortunately for the early prospects of TPOEM they duly achieved an unlikely result at the Brittania.

I will continue to test TPOEM’s predictive efficacy vs bookmaker odds but for any followers of the blog, please note that I am seeking value not outright wins. Even if Manchester United are heavy favourites to win at home, as they were at the weekend, I may suggest another outcome if the odds are attractive enough depending on what my early-stage model tells me!

Defence Against Dribbling: 2012-13

This is a follow-on from my previous post on the top dribbling teams and players this season in the Premier League.  Since I took the time to prepare the data to review the best dribblers with the ball at their feet I thought why not flip the information to review the opposition as well?

This is not a completely straightforward exercise because the information I have available does not identify the opposing player(s) involved when a dribble is attempted – and even if a defender does not actively tackle his opponent, his position may force the attacker into losing the ball (eg. by running out of play or into another defender). I can’t provide much insight on these ‘micro’ events on the field.

But, we can do a similar team analysis as produced in the last post to consider which teams seem to invite dribbling against them, and also which teams are particularly adept at making opposition dribblers lose the ball.

Dribbling Against FrequencyThe above data is ordered by total dribbles against. We can immediately see that teams tend to dribble more often against Sunderland and Norwich (382 and 380 attempted dribbles against respectively) and least frequently against Everton (267 dribbles against). With all teams having played 23 games at the time of writing, this difference of about 5 dribbles per game against perhaps isn’t terribly telling but may give an indication of team tactics without the ball. QPR and Reading, both in the relegation zone, sit at opposite ends of this table.  Alternatively, Everton and Spurs are on the low end, allowing the fewest number of dribbles against, whilst Arsenal are 4th highest despite their league positions of 5th, 4th and 6th respectively. I’m intrigued by this, and admittedly I haven’t given it a lot of thought before just typing away now, but at a guess team pressing plays a part here – Moyes’ Everton in particular have a reputation for pressing across the pitch whilst AVB’s Spurs are beginning to develop a reputation for pressing high up the field. Maybe that has a bearing?

Of course, there is more to defending than pressing or allowing attackers space to dribble. Indeed, even with tight marking some players will seek to dribble to gain a yard against his opponent on the turn.

Here’s a bar graph comparing total dribbles against (as above) but now vs total shots allowed:

Dribbling Shots Against Frequency

 

I’m not particularly fond of information for the sake of it and I’ll admit that the above graph is a bit of a jumble, but we do get a clearer picture of Arsenal and Reading in particular who allow disproportionately fewer (and more, respectively) shots against when compared to their dribbling against stats – perhaps adding some depth to our understanding of how effective their defensive style has been.

What about the success rates of opponents dribbling against?

Dribbles Against - Success RatesThe above graph tells us how successful the opposition has been at dribbling against the team in question. So, although Everton allow the fewest number of total dribbles against, they have the second highest success rate against – i.e. 53% of dribbles against them are successful. Wigan seem to put up the least fight when it comes to dribbling against with opponents having 54% success on average. But most teams are spread a few points either side of 50%, with a few outliers: Manchester City, Spurs and Southampton – all of whom are no pushovers when it comes to dribbling against. City in particular have an exceptional 37% (now I’m starting to wonder why I didn’t turn the success rate on its head for intuitive purposes!). To translate that, around 1 in 3 attempted dribbles against City are successful, which is the lowest rate in the league this year and highlights their strength across the whole pitch.

Dribbles Against Per GameThis last chart is ordered by the number of successful dribbles against per game, so Everton move up a few places and Manchester City take up the position on the far right – allowing on average just under 5 successful dribbles against per game. Manchester City incidentally have conceded the fewest goals so far this year.

What can we learn from this? Obviously there is more to a game of football than how often you allow the opposition to dribble successfully against you but this is still an interesting objective view of the differing defensive styles/abilities of teams in the Premier League and can no doubt be used in tandem with analysis of other defensive measures to improve our understanding of what tactics have been most effective.

 

 

 

Dribbling wizardry: 2012-13 so far

Dribbling is a much-admired skill in football. The best dribblers in the game are fixed into the history of the sport: players like Stanley Matthews, George Best and Diego Maradona are remembered for their almost superhuman ability to leave defenders trailing in their wake. Beating defenders 1-on-1 excites a crowd in a way only shots and goals can match, but in the same way that a poor effort on goal from a good position can frustrate supporters, losing the ball when dribbling in a good position can be just as frustrating!

In this post I look at some of the best and worst dribblers this season in the Premier League: heroes and villains.

Teams Dribbling frequency

So far this season there has been little correlation between the number of times a team dribbles and whether or not a team gets results. As we can see from the above chart, the top four in terms of total dribbles attempted include Liverpool, Newcastle and QPR whilst league leaders Man Utd are mid-table in terms of dribble frequency.

Liverpool fans are treated to a lot of dribbling at Anfield, but as we can see from their success rates the fans are frustrated more often than not: on average they lose the ball through dribbling about 12 times per game.  Although Arsenal attempt about 1 less dribble per game than Liverpool their average success rate is better: Arsenal have the best mean dribble success per game of c.11 successful dribbles vs 10 unsuccessful. Brendan Rodgers and Arsene Wenger clearly to not mind letting their players dribble more freely than Sam Allardyce. West Ham’s dribble success rate is actually slightly better than Liverpool but they attempt less the half as many dribbles per game.

Dribbling success rateTeam dribbles per game

Some of this is perhaps not neccessarily due to team tactics but the amount of time spent in the opposition’s half. Of course, when in the defending half, players generally know better than to take risks with the ball at their feet – so we would expect dribbling to occur mostly in attacking positions by attacking players. We must bear in mind that the dribble statistic used here gives a simple success/fail result per dribble without any additional information on the difficulty of the dribble, the position on the pitch the dribble was attempted or the actions directly after the attempted dribble.

Aston Villa have the best overall success rate in dribbling (62%) but they have only attempted one more dribble than West Ham – this suggests that Villa players are very selective in choosing when to dribble or at least leave the dribbling to their most skilled players.

On the other end of the scale, Stoke and Swansea have particularly bad success rates when it comes to dribbling: 36% and 37% respectively, meaning that only about 1 in 3 dribbles are successful.

Players

For the tables/data below I filtered out any players who had played fewer than 500mins and attempted fewer than 15 dribbles.

Most frequent dribblers

From the most frequent dribblers table above we start to see the driving forces behind the team stats published earlier. Ben Arfa is arguably the dribbling king of the premier league as he attempts almost 9 dribbles per 90mins and yet still manages a positive success rate of 58% (although as a Newcastle fan perhaps I am biased!). Opposition players know Ben Arfa is likely to dribble when he is on the ball and yet they still don’t usually get the better of him. Suarez and Sterling are clearly the main contributors to Liverpool’s high frequency as they account for more than half of all Liverpool’s attempted dribbles but they both have a below average success rate. This is certainly offset by their high rates of chances created from open play. The surprise success story of the table is Samba Diakité who has a strong 66% success rate despite having a high propensity to dribble.

Most successful dribblersThe most successful dribbler is another Newcastle player: Cheik Tioté, who has a ridiculous 84% success rate. In fact this table is an interesting mix of defensive and attacking players. Diakité again stands out, but also Dembélé and Cazorla who have both attempted more than 60 dribbles and still have an superb rate of success.

Dribblers - don't bother

Last but not least, the above table shows the very worst dribblers this year. Stand up David Luiz. To be fair, 8 of the players in this list of 20 have attempted fewer than 20 dribbles – but David Luiz would certainly be better suited to playing it simple as he has only been successful with 2 of his 15 attempts.

Kightly, Dyer and Kacaniklic are arguably even more frustrating for their managers and fans since they seem to be a little over-confident in their dribbling ability! They each succeed in around 1 in 4/1 in 5 of their attempts to beat a defender. PASS THE BALL!!

Feeding off scraps in the Premier League?

Having looked at the top scoring strikers in the league in a previous post on the race for the golden boot, I now turn my attention to shooting statistics for the leading target men at teams in the bottom half of the table. Players for these teams often ply their trade as a lone striker, with less than average support from midfield. As a result the pressure on them to score every gilt-edged chance is high since every goal is precious for their club to ensure survival.

After only 16 games of the season played these players all have 6 goals or less, so each goal or missed opportunity has a strong bearing on their stats (disclaimer!).

The strikers considered this time round are Djibril Cissé (QPR), Christian Benteke (Aston Villa), Adam Le Fondre (Reading), Arouna Koné (Wigan) and Rickie Lambert (Southampton). Cissé, who has played the least in terms of outfield minutes, has also scored the least with only 2 goals for winless QPR. Rickie Lambert is the most prolific goalscorer so far with 6 goals for Southampton. At the time of writing QPR sit 20th in the league with 7pts and Reading just ahead of them on 9pts, whilst Wigan, Aston Villa and Southampton are all level on 15pts. All stats correct as at 12 December, using EPL Index / Opta data.

Efficiency 11 Dec Goals & Shots per 90 11 Dec

Goals & Shots per 90 Data 11 Dec 2Of the 5 strikers, Arouna Koné takes the fewest shots with only 2.49 per 90mins, on average this is far less than Cissé, Benteke and Le Fondre, who each manage to shoot over 3.5 times per 90mins. But shots alone do not necessarily indicate the quality of opportunities on hand – indeed the current league top scorer Michu currently has a shots per 90mins rate of 3.13. Cissé’s low shots on target rate at under 30%, of which only a paltry 20% have been goals, has not done much to help QPR’s cause.

Le Fondre and Lambert are easily outperforming the others from this perspective because the quality of their shots is shown to be generally much higher – and so although they take fewer shots per game their goalscoring rates are significantly better off (c0.45 goals per 90mins). Lambert has a particularly good record of making the opposition keeper work when he has a shot: he has hit the target 47.4% of the time.

Big Chance Data 11 Dec

Big Chance Economy 11 Dec Big Chances 11 Dec

When it comes to big chances, Koné in particular fares poorly.  Although both he and Cissé have a conversion rate of 25%, Koné has had several more gilt-edged chances than Cissé (12 vs 4 respectively). Roberto Martinez will no doubt be disappointed by the return from Koné, however on the plus side the sheer frequency of big chances he is involved in may be a positive sign for the team’s prospects. The small sample size for Cissé means that his conversion rate of 25% perhaps does him a disservice at this point in time – if he scores his next one it’ll jump up to 40%.

Benteke, who in recent weeks has kept Darren Bent out of the Aston Villa team, does not perform particularly well in this analysis. Judged purely by the stats in this post he resembles Cissé much more than Lambert, with below average shooting accuracy and below par big chance conversion.

Of the group, unsurprisingly it is Lambert again who does best with big chances with an excellent conversion rate of 75% (3 from 4). When Southampton have needed him most so far he has come up with the goods, but whether that form continues for the rest of the season is another matter.

Premier League Top Scorers

Using Opta/EPL Index data, I have looked at shooting metrics for the current top 5 goalscorers this season in the English Premier League.

With 4 players on 10 goals, and Jermain Defoe in 5th place with 9 goals, there would seem to be little to tell the players apart. I have presented some simple ratios below to dig a little deeper into how we might compare their shooting efficiency.

Graphs created below as at 05/12/12:

Shooting stats 5 Dec 2012
Big Chance stats 5 Dec 2012

  • Michu (full name Miguel Pérez Cuesta) stands out as being the only player on the list considered by many to be more of a midfielder than a striker. For me, he should be ranked #1 in this shortlist because of his excellent goals rate despite having on average only 2.79 shots per 90mins. He is currently scoring 56% of his shots on target which shows that he has made his opportunities count. This is backed up by an excellent 67% conversion rate of big chances to goals.
  • Suarez has a very high shooting rate per 90mins, averaging just under 6 shots per game, however he has played more minutes than the rest of the group and therefore has the lowest rate of goals per 90mins.
  • Demba Ba has the highest shots on target per 90mins, and he has the lowest playing time, both contributing to give him the best goals rate per 90mins stat: 0.78 per game. However, his goals per shots on target rate is equal lowest with Suarez.
  • RVP has had the highest rate of big chances per 90mins at 1.67 but he also has the lowest conversion rate at 33%. No doubt he will be looking to improve this as the season develops.
  • Defoe is rather trigger-happy (still not quite at Suarez’s level!) but his shots on target per shot ratio is more healthy at 38% of shots. He also has the second highest big chance economy rate with currently 56% of big chances converted that have fallen his way.