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Saturday, 24 June 2017

You Don't Need Goals to Change Game State

I’ve written previously about the concept of game state and how a side prioritises their attacking and defensive resources.

It is well known that trailing sides often increase their attacking output when they are behind compared to when they were either level or ahead and this in turn impacts on the amount of defending their opponents are obliged to do.

Dependent upon the relative abilities of the two competing teams, a side seeking to get back on level terms often takes more shots and also accrues more products of attacking play, such as corners than was previously the case.

However, game state, as simply defined as the current score line does seem limiting and I’ve previously quoted the example of a top side playing out a goalless draw with a lesser team.

While the level scoreline would be increasingly welcome to the lower rated team as the game progressed, the opposite would apply for the better side in the matchup.

Therefore, quantifying “game state” should perhaps be done in terms that include the changing expectations of each team due to the passage of time and scoreline, rather than simply the scoreline.

I’ve suggested using the expected points each side would get on average from a match as a suitable baseline with which to begin measuring the evolving state of the game.

Here’s an example.

Chelsea entertains Everton and based on pregame home win/draw/away win estimations, Chelsea would expect to average 2.1 points compared to around 0.71 points for the visitors from the fixture.

40 minutes into a still goalless game and these numbers have respectively fallen to 1.9 and risen to 0.81. After 67 minutes and still no goal and Chelsea are faring even less well (1.66) and Everton are up to an average expectation of 0.90 points.

There have been no goals, but the state of the game is constantly drifting away from Chelsea’s expectations and surpassing Everton’s “par for the course”.

Chelsea's game state environment is gradually becoming less palatable to them and Everton's more so, simply through the passage of time and if this feeds through into the relative approaches of the sides, it should be seen in the match data.

Here’s a memorable 0-0 from 2016/17 when Burnley took a point in a stalemate at Old Trafford.

The host’s average expected points total started at around 2.3 points at kick-off compared to 0.55 points for the visitors, but it had fallen by over 10% when half time failed to see a score. So a gradual erosion of expectations, rather than a precipitous decline.

Burnley’s modest expectation was up to over 50% of their original with 20 minutes remaining and with United’s now tumbling by nearly a quarter compared to kick-off, their shot count began increasing as Burnley’s stalled.

        How Manchester United Piled on the Attempts as Burnley Frustrated them at OT.


This switch towards a more overtly attacking stance from the side leaking initial expectation as time elapses in a level match, forces their opponent to adopt a more defensive outlook and appears to be mirrored, on average in all such matches from the 201617 Premier League season.

72% of the goal attempts taken when the scoreline was level in 201617 were taken by the side whose expected points had slipped below their pregame estimation. Perhaps an important consideration when nearly half of all goal attempts from 201617 came while the scores were level.

Across all score lines, the inferior team in a match who had managed to improve their pre-game position, either through remaining level or taking a lead, attempted 31% of shots while that position persisted, but such sides upped this to nearly 46% against superior opponents when their current points expectation fell below their initial expectation.

These figures tally with intuition about how games develop, even in the absence of goals.

Therefore, the amount of change in a team’s pregame expectation may be a viable extension to the more commonly applied mere scoreline when assessing game state, particularly when we are still awaiting an initial goal.

For example, it is commonly assumed that increased shot volume from a side that finds themselves in a disadvantageous game state is partially balanced by a more packed defence.

This may lead to the expected goals from identical pitch locations being lower when defensive pressure is greater.

To try to test this I included a variable for game state within an expected goal model.for the 201617 Premier League, based around this continuous, time elapsed and score dependent calculation, rather than merely using the current scoreline.

Overall, a team playing with a current expected points total that had dipped well below their pre-game expectations, converted chances at a lower rate than identical chances where game state was much less of a factor.

In addition, as teams played with a poorer game state, their goal attempts were also more likely to be blocked by defenders than in similar situations when their game state environment wasn't as dire.

As an example, a side who had improved their position compared to pre-game by around 40% of their initial points expectation might convert a decent shot from the heart of the penalty area around 44% of the time.

But when faced with the same chance when their points expectation had fallen by a similarly large amount, they appear to only convert the opportunity 37% of the time.

This may be due to fewer defenders being around in the first instance as their opponents perhaps chased a goal of their own compared to the second situation when defence might be a higher priority for their opponents.

Thursday, 15 June 2017

Early Season Strength of Schedule

With the major European leagues currently enjoying their summer holidays, it is left to a handful of competitions to provide club based action until early August.

One such league is Brazil's Serie A, a fascinating mix of player and managerial churn, exciting skillful youngsters, paired with former internationals, slowly winding down their illustrious careers and lots of shooting from distance.

Tonight sees the completion of week seven of the twenty team league, so while we have accumulated some new information about the 2017/18 version of teams such as Santos, Sao Paulo, Corinthians and less know sides, such as Gremio and Bahia, that information comes courtesy of an unbalanced schedule.

Prior to week seven, Flamengo had played three of the current bottom four and no side from the top half of the table, whereas Vasco da Gama had faced the current top two and only two sides outside the top ten.

The challenges faced by these two sides were likely to vary in their degree of difficulty,

Delving deeper into each side's most recent games, including matches from 2016/17 may be a more reliable indicator of their respective future prospects, but it is understandable that a six game season to date also invites comment in isolation.

Predicting the future arc of a team's season is always welcome, but celebrating achievement over a shorter time frame, even if some of it has come from a sprinkling of unsustainable randomness also deserves attention.

How can advanced stats and strength of schedule adjustments assist?

It's natural to look firstly at the record of the side in question, but it is their opponents that possess the richest seam of data from 2017/18's fledgling season.

Vasco has played Palmeiras, Bahia, Sport, Fluminese, Corinthians and Gremio prior to last night and in turn each of their opponents has also played five other opponents in addition to Vasco.

Combined, Vasco's opponents have played 36 games, nearly a full season and have played every side in Serie A at least once, bar Corinthians.

We have a ton of accumulated data from goals to expected goals for Vasco's opponents, but only six games of data for Vasco themselves and the same is true for the remaining 19 teams.

It's natural to expect even this limited, if recent achievement does contain some signal relating to future performance and Ben Cronin over at Pinnacle has written this article about the correlations between Premier League position after six games and final position and the FT's John Burn-Murdoch also tweeted this excellent visualisation correlating current league position during the 2013/14 season with finishing position in May.

To adjust for strength of schedule, we might take expected goal differential, rather than league position as the performance related output for each team and utilise the interrelated collateral form lines are created after a few weeks of the season

Team A may not have played team B yet, but they may have played team C, who have played team B.

We are left with 20 simultaneous equations, with a side's opponents on one side and their actual expected goal differential output on the other. Solve these we have new expected goals differentials that more fully represent the difficulty of each team's schedule.

In short, it is the basis for so called power ratings.



Here's how Serie A teams were ranked by expected goals differential prior to week seven and how that ranking changed when we allowed for the sometimes heavily unbalanced schedules played.

Vasco were ranked 13th on expected goal differential, but jumped into the top 10 to 9th when their harsh early schedule was applied.

Ponte Preta dropped four places to 15th in view of an apparently benign group of initial opponents.

In theory this seems fine, but does schedule strength add anything to our knowledge of a side going forward if we choose to limit ourselves to data from just this single season?

As Ben and John have admirably demonstrated, there is a correlation between league position at various stages of the season and finishing position.

Here's a limited (due to workload) example from a previous Premier League season using simply goal differential rather than expected goals.

13 games into the 2013/14 season, Spurs were ranked 13th by goal difference, 10th when strength of previous schedule was applied and 9th in the actual table. They finished 6th.

Their position in the table after 13 games better predicted their finishing spot, followed by strength of schedule adjusted goal difference and lastly actual goal difference.

As a whole though ranked, strength of schedule adjusted goal difference from week 13 did best of the three, producing ranked correlations of 0.77 for league position and actual goal difference after 13 games, but rising to 0.80 when strength of schedule corrections were applied and the teams re ranked after 13 matches each.

In short, there is signal in limited early season data and as a means of predicting final finishing position there may be some improvement if we rank by a schedule adjusted performance indicator.

All Brazilian data from InfAppoGol

Sunday, 11 June 2017

Take On Me

A quick data viz spin through some of the less readily available attacking stats from the 2016/17 Premier League.

Aside from a penalty kick, the take on is the contest in a football game that most directly pits together the attacking and defensive attributes of individuals.

The ability to break apart a defensive structure by beating an opponent in a one on one contest is a hugely valuable asset, particularly if it takes place deep into opposition territory as demonstrated by England's opening goal against Scotland.

Similarly, conceding possession from an attacking move can also leave a side vulnerable to counters.

So who's perpetually trying to be creative in the opposition box and who might leave his side vulnerable to a costly turnover in less advanced areas of the field.

Here's the plots for the Top Six. The left hand side of the plot is closest to the opponent's goal and players who have played few minutes have been omitted.







Data from InfoGolApp