Allsvenskan 2016 update

With three rounds of Allsvenskan games played since my last post, it’s time for an update. Like last time, I’m just going to throw a few visualizations at you together with my initial thoughts without going too much in-depth.

Starting out with the league table, we see just how close the league has been so far – with eight games played, only four points separate Östersund in 11th place from Malmö in 2nd.

allsv_update_01_01

We can also see some interesting streaks since last time, with Norrköping and Elfsborg winning all three games while Hammarby, Gefle and Falkenberg have been struggling. Looking at the early surprise teams we see that Sundsvall have continued to perform well while Jönköpings Södra have dropped in the table.

allsv_update_01_02Looking at shots we see how Hammarby, Kalmar and Norrköping have all moved in to the ‘busy attack, quit defence’ quadrant, indicating that they’ve played a bit better lately (or faced easier opposition!), while Sundsvall is still stuck in the ‘quiet attack, busy defence’ quadrant.

allsv_update_01_03While Malmö produces a lot of shots, they’re still one of the most ineffective sides up front. Göteborg and Norrköping on the other hand are enyoing some effective scoring at the moment.

allsv_update_01_04Sundsvall are still conceding a lot of shots, but at least they’re not converted into goals very often – which in part explains their good results so far. Elfsborg have moved into the ‘formidable’ defensive quadrant, only conceding one goal in the last three games.

allsv_update_01_05Looking at Expected Goals, Malmö are still the clearly best team, with Norrköping improving while Djurgården have dropped a bit. Here we really see the difference between the early surprise teams’ performance recently, as Sundsvall have improved both attacking and defensive numbers while Jönköpings Södra have done the opposite.

So how would the teams rank xG-wise? Expected Goals Difference should do well as measure of skill, and here we again see how the model ranks Malmö as the best side so far, with Norrköping and AIK the main contenders. A bottom three of Helsingborg, Gefle and Falkenberg have also emerged.

allsv_update_01_06

Another way of evaluating the teams’ performance so far is to simulate how many points on average each team would’ve received from their games. To do this I’ve used the shots from each game to simulate the result 10,000 times and the teams have then been awarded Expected Points based on the derived 1X2 probabilities.

For example, if the simulation would come up with probabilities of 0.5, 0.3 and 0.2 for each outcome then the home side would be awarded 0.5*3 + 0.3*1 or 1.8 Expected Points, while the away side would get 0.2*3 + 0.3*1, or 0.9 Expected Points.

Here’s a table of the team’s Expected Points so far:

allsv_update_01_07But a team can’t get 1.8 points from a game, only 0, 1 or 3 – so how have the teams performed compared to their Expected Points?

allsv_update_01_08Note: Malmö have been awarded a 3-0 win against Göteborg as the game was abandoned due to home fans throwing pyrotechnics towards a Malmö player. These points have been included.

Here we see how Helsingborg and Sundsvall have taken quite a lot more points than expected, while Falkenberg and Kalmar have done the opposite. This could be the result of some good/bad luck, but it can also mean that the model fail to properly assess the quality of these teams.

Let’s dig deeper and have a look at the Expected Points distribution of each team:

allsv_update_01_10Looking at these distributions we can see just how extreme the results have been for some of the teams so far. In fact, my model estimates that if we re-played Helsingborg’s games 10,000 times, they would get 13 points or more only about 5% of the time!

Lastly, here’s my updated prediction of the final Allsvenskan 2016 table:

allsv_update_01_09

That’s it for now. I hope to be back with another update when the league have gone on break for Euro 2016, and maybe I’ll look closer at individual players then.

 

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Allsvenskan 2016 update

Allsvenskan 2016 so far

With 5 rounds of games played I thought it would be a good time to look at how the 2016 Allsvenskan is going. Let’s have a look at the league table so far:

2016_00

True to it’s rather unexpected nature, the opening five rounds of the 2016 Allsvenskan have seen some surprises, and I don’t think anyone expected Sundsvall and newly-promoted Jönköpings Södra to be at the top! Also, last year’s top team’s have been struggling a bit, but seem to have picked up the pace lately.

But nevermind the table – altough it never lies, it does give an unfair view of the teams’ underlying performances, especially with so few games played. To really have a look at how the team’s been coming along so far I’ve reproduced some of Ben Mayhew‘s beautiful scatterplots:

2016_01

Looking at shots taken and conceded per game we can see how Malmö, Djurgården and AIK have dominated their games so far, outshooting their opponent’s by some marginal. League leaders Sundsvall have, given their results, surprisingly spent most of their time in defence – but that’s just how Allsvenskan is.

 

2016_02

When looking closer at shooting effectiveness we see that surprise teams Sundsvall and Jönköpings Södra have been clinical in front of goal so far, partly explaining their results. Häcken on the other hand have really struggled to score.

 

2016_03

Looking at defensive effectiveness we can really see why Sundsvall are at the top of the Allsvenskan table. While spending a lot of time in defence, they’ve managed to concede very few goals given their shots faced. If this is down to some new tactic, skill or simply dumb luck remains to be seen – but for a team like Sundsvall I’m willing to say it’s the latter.

 

2016_04

Expected Goals-wise we see just how lucky Sundsvall have been so far. They’ve conceded a lot of xG while failing to produce up front, putting them in the same group as struggling sides Falkenberg, Häcken, Helsingborg and Gefle. Malmö is at the other side of the scale, producing a lot of high-quality chances while keeping a tight defence.

Another interesting thing to look at is time spent in Game States. As a result of their good performance (or luck!) so far, Sundsvall have only spent about 1% of minutes played losing so far while Gefle have only spent 10% in the lead!

2016_05

What about a prediction for the rest of the season then? I’ve used the games so far to fire up my league table simulation based on my Monte Carlo xG game simulation, and this is the result:

predict_2016_05

Note: The first table posted here was wrong due to a minor error in the code. This is the correct table.

As a Djurgården supporter, I kinda like the result – even though I think it’s a bit unrealistic for us to compete for silverware just yet. And anyways, a simulation of the whole season based on only 5 games tells more about what has happened so far than what we’ll see in the future, at least in my opinion.

The model clearly ranks Malmö as the best team in the league, as it’s done pretty much every season in my database, alongside AIK and Göteborg. Both the newly promoted teams, Östersund and Jönköpings Södra, seem competent xG-wise and have a good chance of staying up, while reigning champions Norrköping seem to be performing worse than last year. Gefle are always in the bottom of these kind of tables, but nevertheless seem to outsmart every metric available to avoid relegation season after season – but maybe this is the year they finally drop down to Superettan?

I’m planning to do these kind of updates at regular intervals, and maybe add some more plots and deeper analysis, but this will have to do for now!

Allsvenskan 2016 so far

A rough prediction of the new Allsvenskan season

Though I hadn’t planned on posting a prediction for the new Allsvenskan season until a couple of rounds had been played, after seeing Per Linde of fotbollssiffror posting his prediction on twitter and mentioning how he disagreed with it, I decided to do the same and fire up my league table prediction script from least season.

My Monte Carlo game prediction is designed to use at least a couple of rounds of data, so I was unsure how it would go about predicting a new season right from scratch, but I actually think it turned out better than expected:

predict_2016_01

There are some obvious problems though. First off, the script still thinks it’s 2015 and Jönköpings Södra and Östersund are playing in Superettan, causing some strange error where their every game is simulated as a 0-0 draw. This obviously skews the prediction for every team, but it isn’t really an error as the league simulation script isn’t designed to involve different leagues, and it’ll will be corrected when I update the database with the weekend’s results.

Also, every game is simulated with the teams’ squads as they were at the end of the 2015 season which is obviously a problem, with a lot of players coming and going since then – but again this will be fine once I update the database.

What about the actual prediction then? Besides the error with the promoted teams the only problem I have subjectively is the high percentages for Gefle’s relegation (they’ve been ruled out as long as I can remember but have still managed to stay up year after year) and Norrköping’s title defence. I’d also switch places between Djurgården and Häcken while placing Örebro somewhere in lower mid-table. The promoted sides are hard to predict, but I definitely place Östersund above Jönköpings Södra.

Though I’m pretty happy with the prediction, I’ll update it in another post once a couple of rounds have been played.

A rough prediction of the new Allsvenskan season

How important is the starting line-up when predicting games?

As I mentioned when doing the betting backtest for my Expected Goals model, my Monte Carlo game simulation is done on player level to account for missing players, which in theory would affect the game a lot. The simulation involves a very simple prediction of the starting line-up for each team in each game – but how would the backtest result look if I somehow could look into the future and actually know which players would be starting the game?

To test this I’ve simulated every game from the 2015 Allsvenskan season again, using my second model with more heavily weighted home field advantage – but this time used the actual line-ups instead of having the model guess. For the backtest I’ve again used odds from Pinnacle and Matchbook, but won’t bore you with the results from both as they’re much the same. Here’s the model’s results betting at Matchbook:

lineup_01lineup_02

As expected, knowing the correct line-up really boosts the model’s predictions, as it now makes a profit pretty much across the board. Just like with the previous backtests, the 1X2 market looks ridiculously profitable as the model is very good at finding value in underdogs.

Let’s compare the results with that from Model 2:

lineup_03

The numbers in this table represent the net difference in results for the two models. In general, Model 3 makes fewer bets at lower odds, but has a much higher win percentage – hence the bigger profit. Remember, the only difference between these models is that Model 3 uses the actual line-up for each game, while Model 2 have to guess.

So could these results be used to develop a betting strategy? Using the actual line-ups for the simulation, the opening odds are of course not available to bet on since they are often posted a week or so before each game while the line-ups are released only an hour before kick-off. But as the game simulation only takes about a minute per game, it’s certainly possible to wait for the line-ups to be released before doing the simulation and then bet whatever the model deem as value.

How important is the starting line-up when predicting games?

World Premiere(?): Expected Goals for Finland’s Veikkausliiga

A while back I stumbled upon shot location data for Finland’s top league, Veikkausliiga. I haven’t seen an Expected Goals model for this league before so despite having no interest in or knowledge of the league, I decided to develop a model for it based on my Expected Goals model of Swedish football. My idea is that a model could be a very useful tool and make a big difference when betting these smaller, lesser-known leagues.

Unfortunately only one season of data is available and like with the Swedish data no distinction is made between shot types beside penalties. But the overall quality seems to be of a higher standard than it’s Swedish counterpart and the data also contains more detailed player metrics like number of accurate passes, fouls, turnovers, etc., which might prove useful in the future.

Model results

FIN_01

First off I’ve tested if the Finnish data is significantly different from that in my Swedish model. It turns out it is, but as one season of data is probably not enough to develop a decent model, I’ve opted to add the new data to my existing model and use it for Veikkausliiga. No Finnish data will be used when dealing with Swedish games however.

Let’s look at some plots of how the model rates the teams and players in Veikkausliiga:FIN_02FIN_03FIN_04FIN_05

Data from the Swedish leagues is colored red and not included in the regressions.

FIN_06

What we can see is that the r-squared for xG/G are worryingly lower than the Swedish model’s 0.61. Also, the model does a better job explaining team defence than attack, just like the Swedish model. Why that is I don’t know.

The model rates HJK as the best team in terms of both xG and xG against but they only finished third – albeit just two points below champions SJK, who seem to be over performing massively with their goal difference about 13 goals higher than expected.

At the bottom of the table, KTP seem to have over performed while demoted Jaro under performed. Mariehamn also seemingly under performed both in attack and defence.

FIN_07
FIN_08Looking at individual players, I’d say the model performs well with an r-squared of 0.8, similar to that of the Swedish model. RoPS’ Kokko had the highest xG numbers to go with his title as top scorer, and interestingly all players in the top 10 in goals outscored their xG numbers.

Betting backtest

While the model doesn’t seem to be as good as my Swedish model, I still think it’s reasonably good considering only one season of data from the league is used. But what about its performance on the betting market?

Just like I did with Allsvenskan, I’ve simulated each game using my Monte Carlo method for game simulation. Obviously only using data available prior to each game, my method rely heavily on long-term team and player performance and my initial guess was that using it for the 2015 Veikkausliiga wouldn’t be profitable since there’s not enough data. Well, let’s see.

backtest_09

Running the backtest my suspicion immediately proved right, as can be seen on the above plot. The model looks like a clear loser, and setting a minimum EV when betting doesn’t seem to change that. But looking at the plot, there’s actually a point late in the season where the model start to perform better.

Since the model was at a huge disadvantage from the start with so little data (the Allsvenskan backtest used four seasons of data), I’ll allow myself to do some cherry picking. Here’s how the model performs betting Pinnacle’s odds after the international break in September:

backtest_10

backtest_11backtest_12

Just like before, Model 2 is just a variation of my Monte Carlo game simulation where home field advantage is weighted heavier. Like with Allsvenskan, both models seem to focus on underdogs and higher odds. What is encouraging is that this time only a minimum EV threshold of 5% is needed to single out a reasonable number of bets. In my backtesting of Allsvenskan a threshold of 50% was needed, indicating that the model probably was skewed in some way.

Like in the Allsvenskan backtesting the model makes a killing on the 1X2 market due to its ability to sniff out underdogs. There’s also some profit to be made on Asian Handicaps while only Model 1 makes a profit betting Over/Unders.

I’ve also run the backtest against Matchbook’s odds, but while I won’t bore you with more plots and tables, what I can say is that the results again match up to my findings from the Allsvenskan backtesting. At Matchbook, betting the 1X2 market is still hugely profitable, the Asian Handicaps close in on odds around even money while Over/Unders perform better, albeit only on closing odds.

Conclusion

As expected, betting on Veikkausliiga from the start of the season would’ve proved a dismal affair. This is understandable since my method rely so heavily on long-term performance and using only a couple of games for assessing player and team quality isn’t a good idea.

But the model did seem to perform better late in the season, and while this probably isn’t enough for me to use it for betting on the upcoming 2016 Veikkausliiga season, I’ll keep my eyes on its performance against the market and maybe jump in when it seems to be more stable.

 

World Premiere(?): Expected Goals for Finland’s Veikkausliiga

Putting the model to the test: Game simulation and Expected Goals vs. the betting market

With the regular Allsvenskan season and qualification play-off both being over months ago, instead of doing a season summary (fotbollssiffor and stryktipset i sista stund have already done that perfectly fine), I thought I’d see how my model has been performing on the betting market this season. Since my interest in football analytics comes mainly from its use in betting, this is the best test of a model for me. Though I usually don’t bet on Allsvenskan, if the model can beat the market, I’m interested.

Game simulation

To do this, I should first say a few things about how I simulate games. I want my simulations to resemble whatever they are supposed to model as much as possible, and because of this I’ve chosen not to use a poisson regression model or anything remotely like that. Instead I’ve build my own Monte Carlo game simulation in order to emulate a real football game as close as possible.

I won’t go into any details about exactly how the simulations is done, but the main steps include:

  • Weighting the data for both sides to account for home field advantage.
  • Predict starting lineups for each team using their most recent lineup, minutes played and known unavailable players.
  • Simulate a number of shots for each player, based on his shots numbers and the attacking and defensive characteristics of both teams.
  • Simulate an xG value for each shot, based on the player’s xG numbers and attacking/defensive characteristics of both teams.
  • Given these xG values, the outcome of the shot is then simulated and any goals are recorded.

Each game is simulated 10,000 times, obviously based only on data available prior to that particular game.

The biggest advantage of this approach is that it’s easy to account for missing players, it is in fact done automatically. It also seems more straightforward and easily understood than other methods, at least to me. Another big plus is that it’s fairly easy to modify the Monte Carlo algorithm in order to try new things and incorporate different data. The drawbacks include the time it takes to simulate each game. At 10,000 simulations per game it takes about a minute, meaning that simulating a full 240-game Allsvenskan season would take at least 4 hours. Also, since my simulations rely heavily on up-to-date squad info, such a database have to be maintained but this can be automated if you know were to look for the data.

For each game, the end results of all these simulations is a set of probabilites for each possible (and impossible!?) result, which can then be used to calculate win percentages and fair odds for any bet on the 1X2, Asian Handicap and Over/Under markets.

As an example of how the end result of the simulation looks, I’ve simulated a fictive Stockholm Twin Derby game, Djurgården vs. AIK. Here’s how my model would predict this game if it were to be played today (using last season’s squads, I haven’t accounted for new signings and players leaving yet):

game_sim_01

Given these numbers the fair odds for the 1X2 market would be about 2.31-3.62-3.44 while the Asian Handicap would be set at Djurgården -0.25 with fair odds at about 1.99-2.01 for the home and away sides respectively. The total would be set at 2.25 goals, with fair odds for Over/Under at about 2.04-1.96.

Backtesting against the market

With my odds history database containing odds from over 50 bookmakers and the fact that timing and exploiting odds movements is a big part of a successful betting strategy, it’s not a simple task to backtest a model over a full season properly. I’ve however tried to make it as easy as possible and set out some rules for the backtesting:

  • The backtest is based on 1X2, Asian Handicap and Over/Under markets.
  • Only odds from leading bookmaker Pinnacle and betting exchange Matchbook is used. Maybe I’ll run the backtest against every available bookmaker in order to find out which is best/worst at setting its lines for a later post.
  • Two variations of the Monte Carlo match simulation is tested, where Model 2 weights home field advantage more heavily.
  • Only opening and closing odds are used in an attempt at simulating a simple, repeatable betting strategy.
  • For simplicity, the stake of each bet is 1 unit.
  • Since my model seems to disagree quite strongly with the bookies on almost every single game, there seems to exist high-value bets suspiciously often. To get the number of bets down to a plausible level, I’ve applied a minimum Expected Value threshold of 0.5. As EV this high is usually only seen in big underdogs, this may be an indicator that my model is good at finding these kind of bets, or that it is completely useless.

So lets’s take a look the results of the backtest – first off we have the bookmaker Pinnacle. Here’s the results plotted over time:

Pinnaclebacktest_01

backtest_02

 

backtest_03

We can immediately see from the results table that the model indeed focuses on underdogs and higher odds. Set against Pinnacle, both variations of the model seems to be profitable on the 1X2 market, with Model 2 (with more weight on home field advantage) performing better with a massive 1.448 ROI.

Both models recorded a loss on the Asian Handicap market and only Model 1 made a profit in the Over/Unders – a disappointment as these are the markets I mostly bet on.

The table above contains bets on both opening and closing odds – let’s seperate the two and see what we can learn:

backtest_04

Looking at these numbers we see that both models perform slightly better against the closing odds on the 1X2 market, while Model 2 actually made a tiny profit against the closing AH odds. We can also see that Model 1’s profit on Over/Unders came mostly from opening odds.

But what about the different outcomes to bet on? Let’s complicate things further:

backtest_05

So what can we learn from this ridiculous table? Well, the profit in the 1X2 market comes mainly from betting away teams which suits the notion that the model is good at picking out highly underestimated underdogs. Contrary to the 1X2 market, betting home sides on the Asian Handicap markets seems more profitable than away sides. Lastly the model has been more profitable betting overs than unders.

As we’ve seen, my model seems to be good at finding underdogs which are underestimated, and that at Pinnacle, this bias mostly exist in the 1X2 market, hence the huge profit.

Matchbook

But what about the betting exchange Matchbook, where you actually bet directly against other gamblers?backtest_06backtest_07backtest_08

The 1X2 market seems to be highly profitable at Matchbook too, and Model 1 actually made a nice profit on AH, especially away sides – in contrast to the results at Pinnacle. Also, the mean odds here are centered around even money. Over/Unders again seems to be a lost cause for my model.

Conclusion

As I’ve mentioned, the model seems best at finding underdogs and high odds which are just too highly priced, and looking at the time plots we can see that these bets occur mostly in the opening months of the season. This may be and indicator of how the market after some time adjusts to surprise teams like this season’s Norrköping.

For a deeper analysis of the backtest I could have looked at how results differed for minus vs. plus handicaps on the AH market, and high vs. low O/U lines. Using different minimum EV thresholds would certainly change things and different staking plans like Kelly could also have been included, but I left it all out as to not overcomplicate things too much.

I feel I should emphasize that the different conclusions made concerning betting strategy from this backtest only applies to my model, and not Allsvenskan or football betting in general.

As we’ve seen, an Expected Goals model and Monte Carlo match simulation can indeed be used to profit on Allsvenskan. However, the result of any betting strategy depend highly on not only the model, but also when, where and what you bet on.

Putting the model to the test: Game simulation and Expected Goals vs. the betting market

Preview: Allsvenskan Qualification Play-off

With the regular Swedish season being over and Norrköping crowned champions, all that’s left now is to decide who’ll get the last spot in next years Allsvenskan. In this qualification play-off, Sirius finishing 3rd in Superettan is pitted against Allsvenskan’s 14th placed Falkenberg in a two-game battle.

Let’s have a look at some stats for the teams, compared to both the teams in Allsvenskan (blue) and Superettan (red):

play_off_01

From this graph, Sirius actually look really good with especially a strong defensive, even when compared to the Allsvenskan teams, while Falkenberg’s defence looks really poor. However, this doesn’t say much about how the teams compare to each other since Falkenberg has had to face far tougher opponents in Allsvenskan.

play_off_02 play_off_03

Looking at the xG maps what again stands out is the defensive performances of the teams. While Falkenberg have conceded a massive 415 shots, almost 14 per game, Sirius have only conceded 241 shots or about 8 per game. Not only that, Sirius’ xG per conceded attempt is 0.111 while Falkenberg’s is a staggering 0.154, meaning they concede shots in quite bad (for them) situations – not a good thing.
play_off_04Looking at individual players we can se how Sirius’ Stefan Silva is the big overperformer here with his 12 goals almost doubling his xG numbers. Also, Falkenberg seem to have more goalscoring options with three players over 6 goals while Sirius only have Silva.

As always, I’m not willing to present any prediction for individual games, but here I had hoped to show the results of a simulation covering both play-off games including possible extra time and penalty shoot-out. I have run such an simulation, however I’m not happy with the results as my model seems to be favouring Sirius too heavily. This is almost certainly due to the different leagues involved, making Sirius look way better than they would be against an Allsvenskan side. Since I only came up with writing this post this morning, I haven’t had the time to look into a possible league strength variable to use in the simulation.

But if I had to guess, I’d say that Sirius looks like a real strong side and should possibly be considered favourites for promototion here, mostly due to Falkenberg’s nasty habit of conceding a lot of shots with high goal expectancies.

Preview: Allsvenskan Qualification Play-off