Göteborg

Allsvenskan 2017 Summary Part 2

zorba138, , , , , , , , 1 Comment

Alright, after my last post discussing team performance for the 2017 season it’s now time to dig deeper into Stratagem‘s great dataset and have a look at individual players.

As usual, it’s important to remember that Stratagem collects chances, not shots and also this: only one chance per attacking play is recorded. So for example if a team forces a goalkeeper to a series of saves in a single attack, only the highest rated chance (or a goal if it was scored) is recorded – this of course makes much sense as you can only score a maximum of one goal per attack.

Another difference from most data collectors is that whenever a blocked or saved shot rebounds and leads to a new chance, Stratagem credits the original shooter with an assist for his part in ‘creating’ this new chance. It’s important to note though that this only happens if the rebound chance happens to be of a higher quality than the original chance or end up as a goal, due to the above rule of only one chance per attack.

Lastly, when it comes to minutes played I’ve taken some time to try to calculate it as correctly as possible to get a better look at players ‘true’ performance. Sites like Soccerway seems to set their maximum playing time per match to 90 minutes which is of course wrong as there’s usually a lot of injury time to consider, sometimes even in the first half. So for this post with injury time in both halves taken into consideration you’ll see players which have played more than 30 units of 90 minutes and this also means that most players will see their per90 stats slightly diminished.

All data is open play chances, i.e. penalties are excluded for this post.

But enough of that, let’s get to it and have a look at some numbers. As usual I’ll just throw some plots at you together with my spontane thoughts:

Goalscoring

01Though sharing honors as the league top scorer at 14 with Magnus Eriksson, the moral winner is Norrköping’s Kalle Holmberg with 13 open play goals while 5 of Eriksson’s goals came from penalties. Eriksson’s 9 open play goals is still very impressive though, seeing him finish joint second together with a group of strong goalscorers, all forwards – while Eriksson has mainly been used in midfield in Özcan Melkemichel’s Djurgården.

Another impressive performance comes from AIK’s Nicolas Stefanelli who managed to reach 9 open play goals despite only arriving during the summer, resulting in him topping the league when it comes to goals scored per 90 minutes. Versatile Bjørn Paulsen‘s 8 goals are equally impressive as he’s been used in both central midfield and defence alongside his starts up front for Hammarby.

Tobias Hysén shows that he’s still to be reckoned with, producing the highest total xG in the league at age 35. I’ve been waiting for his performance to drop for some years now, will he surprise me again next season?

The lack of any real xG per 90 Wizard this season (besides Stefanelli, maybe) sees some surprising names break into the immediate top. Johan Bertilsson, Skhodran Maholli (though he enjoyed an initial strong start to his arrival at Sirius) and Linus Hallenius comes to mind. Impressive of course, but it should be noted that this Allsvenskan season has been lacking the strong goalscoring box-player poacher type like pasts seasons’ Kjartansson, Owoeri and Kujovic. Kalle Holmberg could’ve been that player but IFK Norrköping’s weak end to the season has certainly limited his output to more normal levels.

Eflsborg’s Issam Jebali was the end point of most chances for the season, but when playing time is taken into consideration, AIK’s Nicolas Stefanelli once again reigns supreme.

02Comparing goals and xG we see that Stefanelli’s output isn’t that much better than expected, he could very well be the real deal. Another interesting point is that Malmö’s captain Markus Rosenberg continues to underperform against xG.

03.pngLooking at how many chances players create and the average quality of those chances should give us at least some sense of their preferred attacking styles. We see here how most strong attacking players tend to cluster around an area of compromise between quality and quantity. In this group, Viktor Prodell, Johan Bertilsson, Henok Goitom and Mohamed Buya Turay tend to rely more on high quality chances (all above 0.20 xG per chance), while David Moberg-Karlsson and Stefanelli prefer to just rack up chance after chance, the latter with some respectable xG per chance as well.

Moses Ogbu is an extreme outlier with over 0.30 xG per chance, explained in part by the fact that he only took part in Sirius’ very strong first half of the season before getting injured. Still a very interesting player, his numbers would likely have dropped a bit had he been fit to play when Sirius struggled (including 7 straight losses) in the second half of the season.

Chance Creation

04.pngElfsborg’s Simon Lundevall provided most assist overall but taking playing time into account, IFK Norrköping’s Niclas Eliasson was Allsvenskan’s main creator this season. Racking up 11 assists in the first half of the season before leaving for Bristol City in the Championship, his departure effectively ended Norrköping’s top 3 ambitions.

Magnus Eriksson, Tobias Hysén and Nahir Besara‘s appearance in the Assists Top 10 really shows their versatility and huge importance to their teams’ overall attack.

Just like seen with goals above, some interesting and perhaps surprising names appear when we account for playing time. I certainly didn’t expected to see Sirius’ Elias Andersson or AFC Eskilstuna’s Andrew Fox here, but there you go.

Ken Sema‘s strong finish to the season saw him (besides earning a call-up to ‘Party-‘ Janne Andersson’s national team which beat Italy to advance to the World Cup) top the Expected Assists table at roughly 11, though 3 less than his actual output. Sema has also been performing well in Östersund’s Europa League campagin and is one of many players they’ll have to work hard to keep over the winter transfer window.

Nostalgic as I am, it’s certainly nice to see my boyhood hero Kim Källström racking up some strong numbers placing him in the Top 10 Assists and xA tables, as well as creating most chances in the league overall and 4th most when taking playing time into consideration.

05.pngComparing assists and xA we see how Niclas Eliasson has been outperforming his expected output (likely thanks to some effective scoring from Kalle Homberg) while Ken Sema has been underperforming. Lundevall is closer to his expected output.

06.pngJust like with the Attacking Styles, Player Chance Creation Styles are mostly clustered with a lot of creative players combining reasonable quality with quantity. Ken Sema, Elias Andersson and Yoshimar Yotún (who left Malmö for the MLS in the summer) are the extremes when it comes to creation volume, while Andreas Vindheim has created some very good chances for Malmö.

Attacking Production

07.pngBy combining goals and assists into Attacking Production we see that Besara was the most productive player when it comes to raw numbers, but when factoring in playing time, Stefanelli once again tops the table in both expected and actual output. Prodell has done well considering his playing time, as well as Malmö’s Alexander Jeremejeff who’s second behind Stefanelli in xG+A per 90 minutes.

Djurgården’s both wingers break into the Total Chance Production table, with Othman El Kabir joining Eriksson just below the top trio. Paulinho was the most productive attacking player though, creating over 5 chances per 90 minutes for Häcken.

08.pngLooking at actual and expected output, we see how most strong attacking players like Besara, Jebali, Homberg, Eriksson, Hysén and Stefanelli tend to perform close to what we can expected. Eliasson is again overperforming while Rosenberg is doing the opposite. Eric Larsson is worth a special mention here as he has produced some fine numbers for a fullback, with his underperformance coming largely from his teammates in Sundsvall underperforming on the chances he created for them.

09.pngSeperating Expected Goals and Expected Assists let us see how the attacking players specialise. Once again we see how this season has really lacked many strong specialist, with only Stefanelli and Sema really standing out on their ends. Most players tend to cluster somewhat here as well, combining creativity with being at the end of chances as well.

Player Profiles

As I now work with StrataData, I’d thought I’d do a total revamp of the popular player maps. The style is more or less shamelessly stolen from a range of other analysts, no names mentioned, and now also include Chance Creation Maps:

K. Holmberg_chanceAs mentioned earlier, Kalle Holmberg was this season’s strongest goalscorer, and from his Chance Map it’s easy to see why: he usually gets into some very good positions just in front of goal, with an average xG of 0.19 per chance. 13 open play goals is strong, but as I’ve also mentioned I think he could’ve done even better had IFK Norrköping’s performance not dropped (and Niclas Eliasson not left).

M. Eriksson_chanceM. Eriksson_creationOperating from Djurgården’s right wing, Magnus Eriksson was another strong goalscorer this season, though a bit more versatile as he also provided a lot of assists for his team. Mostly crosses from the right flank but also two shot rebounds. His Chance Map is a bit different from Holmberg’s with more chances outside the box, which is only natural as he’s after all a midfielder. Though attacking is certainly his main quality, Djurgården will also miss his work ethic, grit and competitiveness now that he’s left for the MLS.

T. Hysén_chanceT. Hysén_creationVeteran Tobias Hysén continues to be extremely important to IFK Göteborg’s attack. His Chance Map combines a lot of good chances inside the box with some poorer outside, some of them direct free kick. When it comes to Chance Creation he’s provided some crucial passing inside and into the box, as well as some corners and free kicks.

N. Besara_chanceN. Besara_creationÖrebro’s Nahir Besara was also extremely important to his team’s attack, combining some chances inside the box with a lot of shooting from outside, including one goal from a direct free kick. His creation numbers are boosted by three rebounds who turned into goals, otherwise it’s mostly corners and crosses into the box.

N. Stefanelli_chanceNicolás Stefanelli arrived at AIK at a crucial time this summer, with the team’s attack struggling during the first half of the season. The Argentinian took some time to adopt but slowly turned into to a real strong presence up front, scoring 9 goals from 14 starts. It will be very interesting indeed to see if he can continue his fine performance come the new season. As a Djurgården supporter, I sure hope not.

L. Hallenius_chanceLinus Hallenius is an interesting case that’s flown under at least my radar this season. With 7 goals and nearly 10 xG he’s done well for a struggling Sundsvall side that just barely managed to stay up. Most of his chances have been created by Eric Larsson, so it’ll be very interesting to see if Hallenius can continue his fine performance next season with the right back having left for champions Malmö.

S. Lundevall_creationElfsborg’s Simon Lundevall was the assist king this season at 12, with 4 of them coming from corners, curiously with some rather high xA values – 3 of them are above 0.30 xA. Maybe Elfsborg have some corner strong routine going on? Lundevall has also provided some long range passes on the left half of the pitch, which I guess is related to counter-attacking.

N. Eliasson_creationNiclas Eliasson’s strong first half of a season earned him a move abroad, and as mentioned earlier IFK Norrköping never really looked the same after that. Overperfoming, sure, but he did create some really good chances for his team with his precise crossing from both flanks.

K. Sema_creationKen Sema was another creation monster, racking up some really good chances with an average xA per chance of 0.18. It’s clear to see why, as most of his passes was either directly inside the box, or ending up in it – a direct consequence of Östersund’s heavily passing-oriented style of attack.

K. Källström_creationThough it stopped at just one season before he chose to end his career, Kim Källström’s long-awaited return to Djurgården was (despite some very inconsistent perfomances) instrumental in returning the team he once won the league with in two consecutive seasons at the start of the millenium, back to the top 3. When he was at his best this season, sitting back in his deep-lying playmaker role he dictated much of Djurgården’s attack with his quarter-back ‘Hail Mary’ style of long passing. Interestingly though, all his assists came from set pieces where he got more time to use his precise left foot.

E. Larsson_creationI mentioned Eric Larsson before and looking at his Chance Creation Map we see clearly how strong a player he is. From his right back position at struggling Sundsvall he produced 52 chances and well over 6 xA – more than most midfielders. Though his teammates only managed to score twice on these chances, with his move to Malmö I expect him to get a lot more assists next season.

That’s it, thank you for reading the whole piece. If you want to see any more Player Chance/Creation Maps, just let me know on Twitter.

This article was written with the aid of StrataData, which is property of Stratagem Technologies. StrataData powers the StrataBet Sports Trading Platform, in addition to StrataBet Premium Recommendations.

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Allsvenskan 2017 Summary Part 2

Stake sizing, Part 1

zorba138, , , , , , 2 Comments

In the last post we used Python code to take a look at a classic gambling situation, the coin flip, to make a point about the importance of choosing the highest odds available to bet at. Today, we’ll again use the coin flipping example to investigate another fundamental principal of successful gambling: stake sizing.

Now, imagine we’re one of the lucky punters from the last post who were allowed to bet on a fair coin flip at odds of 2.03. As I stated then, this is pretty much like a license to print money – but how much of your bankroll should you bet on each flip of the coin? Knowing that the coin was indeed fair and you would be getting the best of it, a natural instinct could be to bet as much as you could possibly cough up, steal and borrow in order to maximize your profit. This is a poor strategy though, as we’ll soon come to see.

The reason for this is that even if we do have come across a profitable proposition, our edge when betting at a (I’ll empasize it again: fair) coin flip at 2.03 odds is only 1.5% – meaning that for each 1 unit bet we are expected to net 0.015 units on average. This conclusion should be absolute basics for anyone interested in serious gambling, but to make sure we’re all on the same page I’ll throw some maths at you:

The Expected Value, or EV, of any bet is, simply put, the sum of all outcomes multiplied by their respective probabilities – indicating the punter’s average profit or loss on each bet. So with our coin flip, we’ll win a net of 1.03 units 50% of the time and lose 1 unit 50% of the time; our EV is therefore 1.03 * 0.5 + (-1 * 0.5) = 0.015, for a positive edge of 1.5% and an average profit of 0.015 units per bet. For these simple types of bets though, an easier way to calculate EV is to divide the given odds by the true odds and subtract 1: 2.03 / 2.0 – 1 = 0.015.

An edge of only 1.5% is nothing to scoff at though, empires has been built on less, so we’ll definitely want to bet something – but how much?

Stake sizing is much down to personal preferences about risk aversion and tolerance of the variance innately involved in gambling, but with some Python code we can at least have a look at some different strategies before we set out to chase riches and glory flipping coins. Just like in the last post I’ll just give you the code with some comments in it, which will hopefully guide you along what’s happening  before I briefly explain it.

Here we go:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

def coin_flips(n=10000,odds=1.97,bankroll=100,stake=1,bankrupt=False):
    '''
    Simulates 10000 coinflips for a single punter, betting at 1.97 odds,
    also calculates net winnings

    NEW: default bankroll and stake set at 100 and 1, respectively
    now also calculates if player went bankrupt or not
    '''

    # create a pandas dataframe for storing coin flip results
    # and calculate net winnings
    df = pd.DataFrame()
    # insert n number of coinflips, 0=loss, 1=win
    df['result'] = np.random.randint(2,size=n)
    # calculate net winnings
    df['net'] = np.where(df['result']==1,stake*odds-stake,-stake)
    # calculate cumulative net winnings
    df['cum_net'] = df['net'].cumsum()

    # calculate total bankroll
    df['bankroll'] = df['cum_net'] + bankroll

    # if bankroll goes below the default stake, punter will stop betting
    # count times bankroll < stake
    df['bankrupt'] = np.where(df['bankroll']<stake,1,0)
    # count cumulative bankruptcies, with column shifted one step down
    df['bankruptcies'] = df['bankrupt'].cumsum().shift(1)
    # in case first flip is a loss, bankruptcies will be NaN, replace with 0
    df.fillna(0,inplace=True)
    # drop all flips after first bankruptcy
    if bankrupt:
        df = df[df['bankruptcies']==0]

    return df

First off, we’ll modify our original coin_flips function to take our punter’s bankroll and stake size into consideration, setting the bankrupt threshold at the point where a default sized bet can no longer be made. By default, our punter will have an endless stream of 100 unit bankrolls, but if we set the parameter bankrupt to True, the function will cut away any coin flips after his first bankruptcy.

def many_coin_flips(punters=100,n=10000,odds=1.97,bankroll=100,stake=1,color='r',plot=False,bankrupt=False):
    '''
    Simulates 10000 coinflips for 100 different punters,
    all betting at 1.97 odds,
    also calculates and plots net winnings for each punter

    NEW: now also saves punter bankruptcies
    '''

    # create pandas dataframe for storing punter results
    punter_df = pd.DataFrame()
    # loop through all punters
    for i in np.arange(punters):
        # simulate coin flips
        df = coin_flips(n,odds,bankroll,stake,bankrupt)
        # calculate net
        net = df['net'].sum()
        # check for bankruptcy
        bankruptcy = df['bankrupt'].sum()

        # append to our punter dataframe
        punter_df = punter_df.append({'odds':odds,
                                      'net':net,
                                      'bankrupt':bankruptcy},ignore_index=True)

        if plot:
            # plot the cumulative winnings over time
            df['cum_net'].plot(color=color,alpha=0.1)

    # check if punters ended up in profit
    punter_df['winning'] = np.where(punter_df['net']>0,1,0)

    return punter_df

We also want to modify the many_coin_flips function so that it’ll also take bankroll and stake size into consideration, counting up how many of our punters went bankrupt.

We won’t use the compare_odds function here, instead we’ll write a new one to compare stake sizing – but if we ever want to use it again sometime in the future a few minor changes will be needed here as well:

def compare_odds(punters=100,n=10000,odds=[1.97,2.00,2.03]):
    '''
    Simulates and compare coin flip net winnings
    after 10000 flips for 3 groups of punters,
    betting at odds of 1.97, 2.00 and 2.03, respectively.
    Also plots every punters net winnings
    '''

    # create figure and ax objects to plot on
    fig, ax = plt.subplots()

    # set y coordinates for annotating text for each group of punters
    ys = [0.25,0.5,0.75]
    # assign colors to each group of punters
    cs = ['r','y','g']

    # loop through the groups of punters, with their respective odds,
    # chosen color and y for annotating text
    for odd, color, y in zip(odds,cs,ys):
        # run coin flip simulation with given odds, plot with chosen color
        df = many_coin_flips(punters,n,odd,color=color,plot=True)
        # calculate how many punters in the group ended up in profit
        winning_punters = df['winning'].mean()
        # set a text to annotate
        win_text = '%.2f: %.0f%%' %(odd,winning_punters * 100)
        # annotate odds and chance of profit for each group of punters
        ax.annotate(win_text,xy=(1.02,y),
                    xycoords='axes fraction', color=color,va='center')

    # set title
    ax.set_title('Chances of ending up in profit after %s coin flips' %n)
    # set x and y axis labels
    ax.set_xlabel('Number of flips')
    ax.set_ylabel('Net profit')
    # add annotation 'legend'
    ax.annotate('odds: chance',xy=(1.02,1.0),
                xycoords=('axes fraction'),fontsize=10,va='center')
    # add horizontal line at breakeven point
    plt.axhline(color='k',alpha=0.5)
    # set y axis range at some nice number
    ax.set_ylim(-450,450)

    # show plot
    plt.show()

Now, with all our previous coin flip functions taking bankroll and stake size into consideration, we can go ahead and evaluate a few stake sizing strategies with a new function:

def compare_stakes(punters=200,n=10000,odds=2.03,stakes=[100,50,25,10,5,2,1,0.5],bankroll=100):
    '''
    Similar to compare_odds, but here we instead want to compare different
    staking sizes for our coin flips betting at 2.03 odds

    Increased number of punters in each group, from 100 to 200

    Also prints out the results
    '''

    # pandas df to store results
    results_df = pd.DataFrame(columns=['stake','win','lose','bankrupt'])

    # colors to use in plot later, green=1=win, yellow=4=lost, red=2=bankrupt
    colors = [sns.color_palette()[i] for i in (1,4,2)]

    # loop through the groups of punters, with their respective odds
    for stake in stakes:
        # run coin flip simulation with given stake
        df = many_coin_flips(punters,n,odds,stake=stake,bankrupt=True)
        # calculate how many punters in the group ended up in profit
        winning_punters = df['winning'].mean()
        # ...and how many went bankrupt
        bankrupt_punters = df['bankrupt'].mean()
        # lost money but not bankrupt
        lose = 1 - winning_punters - bankrupt_punters

        # append to dataframe
        results_df = results_df.append({'stake':stake,
                                        'win':winning_punters,
                                        'lose':lose,
                                        'bankrupt':bankrupt_punters},ignore_index=True)

    # set stake as index
    results_df.set_index('stake',inplace=True)

    # plot
    fig = plt.figure()
    # create ax object
    ax = results_df.plot(kind='bar',stacked=True,color=colors,alpha=0.8)
    # fix title, axis labels etc
    ax.set_title('Simulation results: betting %s coin flips at %s odds, starting bankroll %s' %(n,odds,bankroll))
    ax.set_ylabel('%')
    # set legend outside plot
    ax.legend(bbox_to_anchor=(1.2,0.5))

    # add percentage annotation for both win and bankrupt
    for x, w, l, b in zip(np.arange(len(results_df)),results_df['win'],results_df['lose'],results_df['bankrupt']):
        # calculate y coordinates
        win_y = w/2
        lost_y = w + l/2
        bankr_y = w + l + b/2

        # annotate win, lose and bankrupt %, only if >=2%
        if w >= 0.04:
            ax.annotate('%.0f%%' %(w * 100),xy=(x,win_y),va='center',ha='center')
        if l >= 0.04:
            ax.annotate('%.0f%%' %(l * 100),xy=(x,lost_y),va='center',ha='center')
        if b >= 0.04:
            ax.annotate('%.0f%%' %(b * 100),xy=(x,bankr_y),va='center',ha='center')

    plt.show()

By default, our new compare_stakes function creates a number of punter groups, all betting on fair coin flips at 2.03 odds with a starting bankroll of a 100 units. For each group and their different staking plan, the function takes note of how many ended up in profit, how many lost and how many went bankrupt.

As we can see on the plot below, the results differ substantially:

01

Just like last time, I want to remind you that any numbers here are only rough estimates, and increasing the size of each punter group as well as the number of coin flips will get us closer to the true values.

So what can we learn from the above plot? Well, the main lesson is that even if you have a theoretically profitable bet, your edge will account for nearly nothing if you are too bold with your staking. Putting your whole bankroll at risk will see you go bankrupt around 96% of the time, and even if you bet as small as 2 units, you’ll still face a considerable risk of screwing up a lucrative proposition. The truth is that with such a small edge, keeping your bet small as well is the way to go if you want to make it in the long run.

But what if some fool offered us even higher odds, let’s say 2.20? First off, we would have to check if the person was A: mentally stable, and B: rich enough to pay us if (or rather, when) we win, before we go ahead and bet. Here our edge would be 10% (2.2 / 2.0 – 1), nearly 10 times as large as in the 2.03 situation, so we’ll likely be able to bet more – but how much? Well, the functions are written with this in mind, enabling us to play around with different situations and strategies. Specifying the odds parameter of our new function as 2.20, here’s what betting at a fair coin flip at 2.20 odds would look like:

02

As can be seen from the new plot, with a larger edge we can go ahead and raise our stake size considerably, hopefully boosting our winnings as well. So the main take-away from this small exercise is that even if you have an edge, if you want to make it in the long run you’ll have to be careful with your staking to avoid blowing up your bankroll – but also that the larger your edge, the larger you can afford to bet.

That’s it for now, but I’ll hopefully be back soon with a Part 2 about stake sizing, looking at a staking plan that actually takes your (perceived) edge into account when calculating the optimal stake size: The Kelly Criterion.

Stake sizing, Part 1

Allsvenskan 2016 – The Endgame

zorba138, , , , , , , , , Leave a comment

Before I continue with another Allsvenskan 2016 update – the last before the season ends – I have some news regarding the blog.

As some of you may know, I’ve been working part time for StrataBet this season, mostly writing game previews for the Norwegian Tippeligaen. As I soon take on a new, full-time job elsewhere I likely won’t have the time to write as much as I want. Also, with my new job focusing on Allsvenskan and Swedish football in general, I may be reluctant to give away too much information to the general public, so the future of this blog is very uncertain.

I’m hoping to continue writing in some form though, and what I do write will likely be closely linked to StrataBet as they’ve given me access to their great dataset.

Allsvenskan 2016 – The Endgame

Ok, so let’s get on with another update. With only 3 rounds left – the next starts tonight – we can see how much of the drama has gone out of the league table since last time. Malmö have retaken the top spot and thanks to Norrköping’s recent poor form the gap down to the title contenders is now 4 points. Sure, both Norrköping and AIK can still theoretically win the title, but I would be very surprised if Malmö let this slip out of their hands, despite the disappointing defeat to Östersund. They do have some disturbing injury problems though…

01

Göteborg have a chance to break into the top-3 and gain a European spot for next season, but this looks even more unlikely with 7 points up to AIK. At the other end of the table the bottom-3 have looked locked in for a long time. Helsingborg still have a chance to overtake Sundsvall, but again I’d be very surprised if this happens. In mid-table we see how Elfsborg, Kalmar and Hammarby have climbed a few spots at the expense of Örebro, Häcken and Östersund.

02

Counting up shots we see how Djurgården surprisingly is the best defensive side when it comes to denying the opposition chances to shoot. We also see how Gefle continue to be very bad and that Örebro still is the main outlier with A LOT of shots both taken and conceded.

03

Looking at effectiveness up front we see few changes since last time. Elfsborg have been slightly more effective with their shooting though, partly explaining their climb in the table. On the other end of the scale, Helsingborg have had a real problem scoring on their chances lately, with ZERO goals since the last update.

04

Looking at defensive effectiveness we see why Djurgården’s ability to deny the opposition chances hasn’t seen them climb into the upper half of the table: They still concede a lot of goals on the chances they do allow. Only bottom-of-the-table Falkenberg are worse. With Malmö and Norrköping’s effectiveness declining since last time, AIK now stands out as the far superior defensive side.

05

Not much have changed in terms of chance quality either – but what is interesting here is that Djurgården is the best defensive side when it come to xG as well. So if they concede very few chances, and very little xG – why are they conceding all those goals? My guess is – I don’t have time to look it up – that the few chances they do concede are of higher quality. Djurgården have also had a lot of problems with goalkeepers this season. Having used 4 keepers so far, only star signing Andreas Isaksson has looked stable enough but he has picked up an injury and will be out for the remainder of the season.

I don’t know much about evaluating goalkeepers but have been thinking about doing a blog post about it for some time now, hopefully I’ll get to it in the near future.

06

Looking at Expected Goals Difference, we see how Djurgården’s lack of defensive effectiveness has robbed them of a nice upper half finish. My model currently ranks them as 5th in the league, close to Hammarby in 4th – far above their current 11th place.

We also see how AIK have overtaken Norrköping in 2nd place, and with the reigning champions in poor form and just 3 points above AIK, this is where most of the drama left in the season lies. At the bottom of the table, Helsingborg are actually ranked far better than Sundsvall above them, but the 7 point gap will likely be too much for Henrik Larsson’s men with only 3 games remaining.

07

The model has always liked Malmö and they actually have the chance to secure the title tonight, if Norrköping lose away to Elfsborg while Malmö win away to Falkenberg – a not too unlikely outcome. In the race for 2nd place, AIK now have the upper hand much thanks to Norrköping’s recent poor results. Göteborg seems to have all but locked in the 4th place and the same goes for the bottom 3.

To continue my slight focus on Djurgården in this post, they’re interestingly projected to take about 6 points from their 3 remaining games: Helsingborg away, Häcken at home and Sundsvall away. Given their very disappointing season, and as a cynical Djurgården supporter, I doubt this.

Allsvenskan 2016 – The Endgame

Preview: AIK vs. IFK Göteborg

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Monday night AIK will host IFK Göteborg for an extremely important game in the race for the Allsvenskan title. Both teams are close behind Norrköping in the lead and will surely go for the win here to challenge for the title, and I thought it would be a good idea to have a look at some team stats as a preview to this crucial game.

The plot below contains goals, shots, Expected Goals, xG per attempt, goal conversion % and shot on target % – both for and against, normalized per game where necessary. Home and away stats for each team in the league are separated with home in blue and away in red. For each subplot the lower right corner is preferable, with high offensive and low defensive numbers.

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Besides SoT%, both AIK and Göteborg appear to be among the best in the league in each stat, which partly explain why they are fighting for the title. What is really striking though, and could be seen as a indicator of team style, is that while AIK’s offensive numbers at home are really good, Göteborg’s strength when playing away is their defence.

aik_gbg_01 aik_gbg_02

This is also evident from each teams xG maps, where it is clear that AIK’s main strength is their attacking power and ability to produce high volumes of shots with high xG values each game. Göteborg on the other hand rely heavily on their defensive skills to protect their box and limit the opposition’s scoring chances. This clash of styles adds yet another interesting flavor to an already interesting game.

aik_gbg_04Looking at each teams top 5 goalscorers it is clear that AIK’s impressive attack rely heavily on Henok Goitom. His 16 goals this season are pretty much in line with his xG of about 15 while Göteborgs Søren Rieks seems to be overperforming with his 10 goals equalling almost two times his xG numbers. Both teams have sold one of their best offensive players with Bahoui and Vibe both making a move abroad this summer.

What about a prediction then? While I won’t reveal any percentages for this (or any) game, what I can say is that my model is pretty much in tune with the betting market. AIK is a slight favourite due to their home advantage, but this is really anybody’s game and it will hopefully be highly entertaining.

Preview: AIK vs. IFK Göteborg

Predicting the final Allsvenskan table

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With the Swedish season soon coming to an end it’s a good time to try out how the Expected Goals model will predict the final table. With only three games left a top trio consisting of this season’s big surprise Norrköping just in front of Göteborg and AIK are competing for the title as Swedish Champion. At the opposite end of the table Åtvidaberg, Halmstad and Falkenberg look pretty stuck, with the two latter teams battling it out for the possible salvation of the 14th place relegation play-off spot.

predict_table_01

Let’s take look at the remaining schedule for the top three teams:

Norrköping have two though away games left against Elfsborg and Malmö, who are both locked in a duel for the 4th place which could potentially mean a place in the Europa League qualification. Elfsborg are probably the tougher opponent here, with reigning champions Malmö busy in the Champions League group stage. Between these two away games Norrköping will play at home against Halmstad who are fighting for survival in the bottom of the table.

Göteborg have two though away games themselves, first off at Djurgården and later a very important game against fellow title contenders AIK. This game will probably decide which of the two will challenge Norrköping for the title in the last round. Göteborg finishes the season at home to Kalmar who could possibly play for their survival in this last game.

AIK have the best remaining schedule of the three top teams, with away games at Halmstad and Örebro on either side of the crucial home game against Göteborg. As mentioned, Halmstad is fighting for their existence in Allsvenskan, while Örebro’s recent great form have seen them through to a safe spot in the table.

At this late stage of the season there are a lot of psychological factors in play, with the motivation and spirit of teams and players often being connected to their position in the table. These aspects are very hard to quantify and have not been incorporated in my model. So my prediction of the table rely solely on my Expected Goals model used in Monte Carlo simulation. I won’t reveal exactly how I simulate games but the subject will probably be touched upon in a later post so I’ll spare you any boring technical details for now.

Each of the remaining 24 individual games have been simulated 10,000 times. For each of these fictional seasons I’ve counted up the points, goals scored and goal differences for every team to come up with a final table for that season. Lastly I’ve combined all these seasons into a table with expected points and probabilities of each teams possible league positions.

predict_table_03

The model clearly ranks Norrköping as the most likely winner with Göteborg as the main contender, while AIK’s chances of winning the title is only at about 18%. The bottom three looks rather fixed in their current positions with Falkenberg having only a 2% chance of overtaking Kalmar in the last safe spot in the table. At mid-table things are still quite open, even though Djurgården’s season is pretty much over with a 89% chance of placing 6th. Malmö seem to have an advantage against Elfsborg in the race for the 4th place, but given their Champions League schedule their chances should probably be less than the model predicts.

I’ll probably be posting updated predictions on my twitter feed after each of the top teams remaining games to see how the results change the predictions.

Predicting the final Allsvenskan table