Using Data from Leagues Around Europe to Improve xG for the SPFL

Expected goals are having a moment right now. The stat most associated with advancedHayes.jpg stats in football has recently gotten think-pieces from outlets like The Guardian, The Telegraph,, and Fan-Sided . Football stats writer Mike Goodman said today on twitter, “I’m so happy that xG is getting increased exposure that I’m gonna grit my teeth and grin through all the re-litigation of its underpinnings.” More people now at least are aware of xG and have an idea what the stat measures than ever before. With each new “mainstream media” (shudders) piece on expected goals, we start to have the same arguments and discussions about the metric that were had years ago.

While the fight over xG’s importance may be starting all over again in places, if you dive deeper into so called “analytics twitter” you will find great pieces discussing efforts to improve the metric. Marek and Nils articles I linked there are the type of work that made me interesting in stats in football in the first place. Furthermore, those pieces are what drove me to want to track these stats for Scottish football when I could not find any publicly available.

With that being said, Christian Wulff and I have been working on improving expected goals in Scotland. The wonderful people at Stratagem have given us more data than we

Moussa Dembele; xG Monster

could have imagined to help accomplish this. Before Stratagem, I was reliant on pulling shot “location” information from the BBC live-tracker of SPFL matches, simply because there was no other public data available. The “Beeb” xG model served it purpose well, giving us a surprisingly decent look expected goals in Scotland when it did not exist before. However, we can now do better.

Seeing honest-to-goodness x an y coordinates for shots (AND passing locations?!) in the data Stratagem sent Christian and I was a coming to god moment. No longer would be be reliant on terms of “center of the box” from the BBC. In addition to using actual x and y coordinates and shot types, we also now had such information as how many defenders were in between the shooter and the goal and how much defensive pressure the shooter was under when shooting.

With this additional info, Christian and I set out to improve our xG model for the SPFL. Common criticisms of xG is that is does not take defenders and defensive pressure into account, so this new Stratagem data would allow us to address this. Good in theory, right? Well, believe it or not, you run in to a sample size issue when you become more granular and only have a season’s worth of data. Trying to get enough shots to come up with a decent xG model for Scotland where 2 defenders were in between the shooter at 37 x, 44 y on the pitch proved to be a challenge. Luckily, the SPFL is not the only league Stratagem has data for.

Sheet 1-3

While it seems to be a recent trend to try and asses how your Gran would do in the SPFL, most would agree that the level of play in Scotland is below leagues such as the EPL, Bundesliga, and La Liga. No shame in that, it is just reality. However, there are plenty of leagues in Europe and around the world that most would agree are at a similar level to Scotland. Leagues such as Eliteserien in Norway, the Swiss Super League, and others. In total, we had 11 leagues worth of data (Turkish, Swiss, Swedish, Greek, Bundesliga 2, Dutch, Austria, Australia, the English Championship, Norwegian, and Scottish league data to be specific) that gave us over 400,000 shots. With data from what I have dubbed the “League of Average Leagues”, we now can use these defensive metrics from Strata and create small zones on the pitch where shots take place to calculate xG values (though thanks to Nils and Marek’s articles I mentioned above, we are now thinking about how to implement their ideas for our model!).

Heat Map

With these figures calculated based on location and defensive pressure, we can develop a heat map similar to above of the probability of scoring a goal. Going from light green, as the least likely to score, to dark red, as the most likely to score, we can see where a team should be trying to take shots from in order to score. The “danger zone” concept is shown well here, with red filling the 8 yard box and the darkest red in between the frame of the goal. Compare that to the green in the sides of the pitch or outside the box. Clearly you are more likely to score in the red areas than the green (I’m looking at you, Fassai El Bahktouri).


With this new model, Christian (on his new stats and tactics vertical from the 90 Minute Cynic, xCynic) and I are also planning on doing some new things and changing some graphs and maps we have done previously. We have made some improvements to our xG game maps, xG and xA player maps, and some new team graphs that we hope will help further understanding both advanced stats in football and Scottish football as a whole.

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.

B.U.R.L.E.Y. Makes His SPFL Premiership Season Predictions

We have arrived upon the dawn of a new Scottish football season. While Scottish football is often the subject of derision by others due to it’s perceived to lack competition for the title (because in leagues like Germany and Spain, it’s REAAALLLY anyone’s ballgame on who will win the league right?), the opening day of the fixtures sees an endless array of possibilities for the season ahead. Before kick off on match day one, it’s a twelve-way tie in the table, with everyone even on goal differential.

With the beginning of the SPFL Premiership season days away, it is time to un-cage the SPFL probability predicting, insult spewing, and your favorite team hating bot that we all know and hate called B.U.R.L.E.Y. If you are new to the high speed world of advanced stats in Scottish Football, I introduced B.U.R.L.E.Y. in the middle of last season (right after Robbie Nielson’s last match with Hearts that saw them beat Rangers 2-0, my how times have changed eh?).

To quickly describe what B.U.R.L.E.Y. does, using the expected goal data that I collect throughout the season, we can come up with the win, lose, and draw probabilitiesburlz for every match (my methods to do this are described in the link above, though I will be tweaking these methods a bit this season which I will describe later). With these probabilities we can get a projected point total for each club in the SPFL up to the split in the league. Since we do not know who each team will be playing after the split, we can then take B.U.R.L.E.Y.’s projected points per game total for the season and multiply that by 5 for the 5 games after the split and we will have B.U.R.L.E.Y.’s projected point total for each club in the Premiership.

With the new season approaching, I wanted to make some improvements to B.U.R.L.E.Y. Last season, B.U.R.L.E.Y. used a club’s average xG per match, average xG conceded per match, the league average xG, and a a club’s xG average home and away. The results were ok. No prizes for predicting Celtic to take the title, but B.U.R.L.E.Y. thought it would finish Rangers-Hearts for second and third, obviously off from what occurred. B.U.R.L.E.Y also missed on relegation, picking Kilmarnock to go down. Though B.U.R.L.E.Y. did think that Inverness CT were relegation playoff bound, while the Jags earned automatic relegation when the season shook out.

With these in mind, I set out to improve B.U.R.L.E.Y. The fine people at Stratagem, who will been providing me with SPFL data that will allow me to do some cool new stuff this season, reached out to me and suggested I take a look at the effect playing at home has on a match. Looking at my data, I noticed that over the past two season, SPFL home clubs averaged 10% higher xG than away clubs. With that in mind, I decided to give all home clubs that 10% bump in xG in B.U.R.L.E.Y.’s calculations.

SPFL xG H_A.png

Another quandary with B.U.R.L.E.Y. was how to come up with the numbers used to calculate B.U.R.L.E.Y.’s projection for Hibernian’s return to the SPFL Premiership. In most leagues across Europe, recently promoted teams often struggle upon their promotion to the top flight. In B.U.R.L.E.Y.’s Norwegian cousin, R.O.N.N.Y., without data for the previous season for newly promoted Eliteserien clubs Sandefjord and Kristiansund, I took the average for the league last year and went a standard deviation down to come up with their beginning of the season R.O.N.N.Y. figures. These numbers made sense due to the lack of data and tendency for clubs promoted to the Norwegian top flight to struggle.

However, we do have data for what Hibs did last year. Yet, I am not sure Hibs being statistically dominant over the likes of Dumbarton, Ayr, Raith, etc in the Championship will tell us much about how they will do in the Premiership. While in most leagues, it is typically smaller yo-yo clubs that are promoted to come up and struggle, we have seen “big clubs” promoted from the Championship the past two season in Hearts and Rangers. We can use these two clubs as guides to what Hibs might do in the top flight.

With this in mind, I took the average figures used for B.U.R.L.E.Y. for Hearts and Rangers in their first year back in the SPFL Premiership. I then took those averagesburlz 2.jpg and added the averages for places 3rd-7th for the last 3 seasons (the range of predicted finishes for Hibs I have seen by various experts in the media). Averaging all of these figures, I came up with the underlying estimates B.U.R.L.E.Y. will be using in his pre-season predictions for Hibs. Fully admitting there’s a bit of guess work in this, I think this is a decent method to try and quantify what the Easter Road club will do this season.

While coming up with figures for B.U.R.L.E.Y. to predict Hibs campaign took some thinking, their Edinburgh neighbors Hearts present a problem for the model as well. The irony is not lost that B.U.R.L.E.Y.’s first appearance came at perhaps the height of Hearts season last year, while now B.U.R.L.E.Y. is back during some very frustrating lows for the Jambos. Last December, it seemed very possible for Hearts to finish second. Fast forward to today, where they have sacked manager Ian Cathro days before the beginning of the season.

xG Cathro

There was clearly a difference in Hearts performance the first half of the season and the second. Under Nielson, Hearts were averaging an xG of just under 2 per match. Under Cathro, that average dropped to 1.67, and even that figure is inflated by xG outputs of over 4 twice against Kilmarnock. If you take out those Killie outliers out, Hearts averaged an xG per match of 1.34 during their time with Ian Cathro as their manager, similar numbers to the likes of Ross County and Inverness CT last season.

Therefore, when calculating B.U.R.L.E.Y.’s predictions for Hearts season, I decided to completely exclude the data from Robbie Nielson’s reign last year, only using the data from Ian Cathro’s time in charge. Of course, the day after these calculations were done, Cathro was sacked. Yet, with having to replace their manager mere days from the start of the season and the difficult start of the season Hearts face, I decided to keep the figures for Cathro’s time only in the calculations.

Burley Table

After all those adjustments were made, I had B.U.R.L.E.Y. spit out the average number of points he thought each team would have at the split after 10,000 simulations of this season in the SPFL (and boy are Leigh Griffiths legs tired, yeah I made that joke two years in a row, so what?). We see perhaps unsurprisingly, B.U.R.L.E.Y. is putting Celtic on top at 88 points at season’s end, 20 points ahead of rival Rangers. Aberdeen move to third according to B.U.R.L.E.Y., while Hibs and Hearts will be neck and neck, with B.U.R.L.E.Y. thinking Hibs will be slightly better, only averaging 0.35 points better over these 10,000 simulations.

B.U.R.L.E.Y. thinks Motherwell will jump into the Top 6 with their potent attack, though their fate will largely hinge onto whether or not the Steelmen can hold onto Louis Moult. In fact, comparing Motherwell’s points per game last season to what B.U.R.L.E.Y. is projecting, the Well has the largest jump in expected points per game from last season to what B.U.R.L.E.Y. is expecting this year. Clearly, B.U.R.L.E.Y is impressed by Motherwell’s attack and is confident enough in their back-line and keepers improving this season.

On the other half of the table, B.U.R.L.E.Y. predicts this is the season St. Johnstone do not make the top 6 after an unprecedented run of success for the Saints, though only projected to finish a point behind Motherwell. He also still sees a gap between the Burlz 3Saints and the rest of the bottom six, while only 5 points separating 8th and 12th place. Killie will be fighting for their Premiership lives in the relegation playoff come seasons end according to B.U.R.L.E.Y., while Dundee fans will have to enjoy winding up their Tangerine neighbors about sending them down at Dens while they can, because B.U.R.L.E.Y. sees them heading “doon” this season.

It is at this point that I would like to clarify that these predictions come straight from the model. I made my “sight unseen” predicted table with just my stats from last season as a refernce. While my “human” predictions were similar to B.U.R.L.E.Y.’s, having the same top 5, there was some differences. I think St. Johnstone will regress this year, but still finish top 6 and Hamilton will be the ones heading down this season. It will be interesting to compare my thoughts to B.U.R.L.E.Y.’s over the season.

Similar to last season, I will be tweeting out individual match B.U.R.L.E.Y. probabilities each week. I will also be publishing polls to see what my twitter followers think will be the result of matches. We will see whether come year’s end whether B.U.R.L.E.Y. or my twitter followers were better prognosticators of the SPFL.

A Statistical Preview of Celtic’s Champions League Opponent Rosenborg

Check out the preview of Celtic from a Rosenborg perspective at our sister site, KroneBall.

When I started writing about analytics and Norwegian football a few months ago on KroneBall with Christian Wulff, one of the things I was pretty confident about was that Rosenborg would steamroll their way to another league title this season. When I was able to get data from the season before, the Trondheim club had an expected goal difference of about 30 better than the club with the next highest total and finished 15 points better in the table than second place Brann. While they failed to make it past the qualifiers for both the Champions League and the Europa Leauge last season, there was not much to suggest that any club would be challenging Rosenborg come March.

However, that has not been the case through 17 games this season in the Eliteserien. While Rosenborg does sit at the top of the table, they are only 5 points ahead of second place Sarpsborg. They have the third best expected goal difference in the league, behind the very impressive Sarpsborg (with a budget of roughly €4,000,000 and a stadium with a capacity of over 4,000) and underachieving Lillestrom. You could certainly claim Rosenborg are fortunate to be in first.

Celtic and Rosenborg are very similar positions in the European football landscape. They are both “big fish in small ponds”, having much greater resources than the rest of the clubs in their domestic league. However, comparing Celtic’s SPFL campaign last season to Rosenborg this year could not be more different. Celtic were never really troubled in Scotland on their way to a treble while Rosenborg are anything but comfortable slightly over half way through the Eliteserien. A better comparison would probably be current Valerenga manager Ronny Deila’s last year at Celtic. Aberdeen certainly gave Celtic a scare that year, but the gulf in resources helped Celtic see off the Dons and take the title. Rosenborg will be able to address their weaknesses in the summer transfer market and likely win the league again, while their title competitors will likely lose their best players over the summer.

Bendtner xG Map

Rosenborg hoped to further cement their position in Norway and make their way to the group stages of at least the Europa League, if not the Champions League by signing Niklas Bendtner. While Bendtner’s name may certainly be known, he has not been all that Rosenborg had been hoping for so far. He has 6 goals thus far in the league and is fifth in the league in xG with 4.95 and 0.41 xG per 90. However, for the money Rosenborg are paying for him, Bendtner was expected to bring much more. Despite Bendtner playing about 400 minutes more, Matthias Vilhjálmsson and Milan Jevtovic have scored 7 goals and Vilhjálmsson has a higher xG per 90 of 0.50.

In the league so far, Rosenborg manager Kare Ingebrigtsen has had Bendtner much higher up the pitch than we have seen him traditionally in his career. Bendtner does his best work in the box, using his physicality and strength to cause opposing back lines trouble. When he is able to get shots off this year, we see on his shot map that 72% of his shots come from the danger zone in the box, where you are more likely to score. However, Bendtner is only getting off 1.9 shots per 90 minutes, which is not enough for a player of his stature, and more importantly of his wage bill. And with an xA total of 0.99, “Lord Bendtner” is not offering much in terms of setting up his teammates with chances.


Meanwhile, Matthias Vilhjálmsson has only played in 825 minutes and is often coming off the bench for Rosenborg, but has more goals, scores them at a faster pace with 0.76 goals per 90, has a similar xG at 4.57 and a better xG per 90 at 0.50. Yet, Bendtner started over Vilhjálmsson in both legs of Rosenborg’s fixture with Dundalk. It was Vilhjálmsson however that came on to score the fixture’s winning goal in extra time for Rosenborg to advance and secure their place with Celtic. If Ingebrigtsen continues to start Bendtner over Vilhjálmsson and plays him in a similar role that he has all season, he might be doing Celtic an unintended favor.

One player Celtic will have to keep a close eye on is attacking midfielder Fredrik Midtsjø. The 23 year old Midtsjø has the highest xG+xA+xSA, or xGAS (tee hee hee) of 7.92 (xG 1.57, xA 4.99 highest at the club, xSA 1.36). He has a xGAS per 90 of 0.50, 4th best at the club. If you were to compare him to a Celtic player, Stuart Armstrong seems like a decent comparison (including good hair game). He is capable of creating his own shot, but he is more often creating for his teammates with a great final pass.

Midtsjo KP map

In the first leg of Rosenborg’s fixture with Dundalk, Midtsjø was deployed by manager Kare Ingebrigtsen on the wing, where he is less effective in my opinion. Perhaps questionably (I am starting to sense a pattern here), Ingebrigtsen has done this a few times in the season and it often resulted in Midtsjø being less effective. In the second leg back in Trondheim, Midtsjø was played in his more effective central role and I would expect to see him there against Celtic. Scott Brown will have to work hard to keep Midtsjø.


Midtsjø will be a player Celtic will need to focus on when Rosenborg are on the attack, but the Norwegians will be missing perhaps their best playmaker against Celtic. Pal Andre Helland, who Ronny Deila said would “be a big loss”, will not be available for either leg due to injury. Helland has the second highest xA total at Rosenborg at 3.62, but has the same xA per 90 of 0.32 that Midtsjø has. Helland also has the second best xSA at RBK at 1.25. Rosenborg will need to find someone to step up in his place and fill the important role he has with the club.

With this injury and Bendtner’s lack of production so far this season, Rosenborg might be looking to keep things secure in the back, hit Celtic on the counter, and hopefully be within striking distance heading back to Trondheim for the second leg. Ronny Deila suggests as much when talking RBK and Celtic’s Champions League match-up, saying in an interview with the BBC “Their best skills in the team are defend and counter attack. That will suit them and that’s what they want to do in Celtic Park – they want to stay a little bit higher than maybe Linfield was doing.” The only issue is, the stats do not necessarily show that is what they do best. Rosenborg’s xG against is slightly better than average in the league, but worse off than 2nd place Sarpsborg, 4th place Brann, as well as mid-table club Odds BK.

Dashboard 1-4

Meanwhile, Rosenborg have the 2nd highest xG for in Eliteserien so far. Similar to Celtic in Scotland, teams in Norway often sit back against Rosenborg and they have to break down packed penalty areas. They do not often have to sit back themselves and try to counter domestically. Their defense has had some troubles this year, giving up 3 goals against recently promoted club Kristiansund after going up 2-0. RBK eventually got a late equalizer to save a point, but this is not the only instance they have shown defensive frailty.

Tromso currently sit second last in the league and have already sacked their manager this season, but they were able to beat Rosenborg thanks largely to the play of speedy Icelandic winger Aron Sigurdarson. The Rosenborg fullbacks have struggled much of theRosenborg Tromso.jpg year when trying to contain opposition with pace. I am an admirer of Sigurdarson, but Scott Sinclair is much more talented than he is. Heck, Johnny Hayes and James Forrest are likely more talented than Sigurdarson. Celtic would be wise to use the overwhelming pace available in their squad to exploit the trouble the Rosenborg fullbacks have with it.

If Celtic is at their best, they should get through Rosenborg and advance to the Champions League Playoff. Of course, random variance can see anything happen over two legs, but the pace and quality Celtic have should be enough to overcome Rosenborg. Perhaps the only question one may have is what, if any the off the field distractions at Celtic will have on the fixture. The xOpinions Yer Da has on the Green Brigade are definitely overachieving on Twitter right now.

With the early exits of Rangers and St. Johnstone from European competition this year, Scottish football supporters and pundits alike resumed the calls for the Scottish season to be played in the summer, such as Norwegian and other Nordic football leagues do. Yet, those calls were not as loud after Swedish club Malmo were dumped out of Europe last round. None of these calls mentioned Rosenborg failing to qualify for both the Champions League and Europa League last season, nor their struggle to get past Irish Champions Dundalk (who also, ironically, play Summer football) last round. There are changes needed in Scottish football, but there is not much evidence that Summer football is what is necessary, and Rosenborg this year show that.

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.

Identifying Possible Break-Out Players By Making League-Wide Comparisons

As I am writing this, Barrie McKay is being sold to Nottingham Forrest and former boss mckayMark Warburton for a rumored £500,000. When I read this, my thoughts immediately went to the video The SPFL Radar made of the compilation of key passes McKay made last season that one would expect would lead to a goal, but did not. Now, I have written countless words discussing the short comings of the strikers Rangers had last season and I will not add to that here. However, the video had me check McKay’s expected assist stats from last season, where I saw he had a 7.84 xA and 0.28 xA per 90. “Pretty good,” was the first thought that came to mind, but then I thought “compared to what?”

By now, most who have been following my journey through stats and Scottish football are probably familiar with terms and concepts like “expected goals” “expected assists“, and “goals per 90 minutes“. If you are not, check out those links above. There seems to be more acceptance of these ideas around Scottish football as time continues to pass. Well, I am about to throw a whole new concept at you. But you have nothing to fear, it builds on what we know rather than something entirely new.

We know expected goals is a better indicator of future success than things like shots or even goals, but it often can be a bit abstract. If I tell you that Adam Rooney had an expected goals of 10.60 in 2016-2017, you can surmise you could expect him to score around 10 or so goals based on his performance. Maybe he will overachieve and scorerooney, maybe he will underachieve and score less, but we could reasonably expect 10 goals. However, does that mean Adam Rooney had a good season? A bad season? A mediocre season? Expected goals by itself does not tell us much when it comes to a player’s performance compared to the league.

While baseball does not have the hold on Europe that it does on North and Central America, one cannot deny that it was an early adaptor of analytics and stats. I have discussed this before when applying the concept of the age curve to the SPFL and I am going to borrow from baseball here again. Baseball has come up with some stats in Runs Created and On-base Plus Slugging that they have realized are better indicators of performance than traditional stats like Batting Average, which certainly sounds like a familiar phenomenon to what football is going through now. They report these stats in traditional percentages, but they also compare a specific player’s stats to the league’s average in that stat with stats like wRC+ and OPS+.

These baseball stats gave me an idea. I would certainly like to know how a striker’s expected goal stats or a midfielder’s xA stats compares to the rest of the league. I decided to apply the same methodology baseball stat nerds apply to wRC+ and OPS+ to football. Now, perhaps proving we surround ourselves in an echo chamber on Twitter, I contacted Rangers Report to see if this idea was crazy or if it made sense. He let me know he was working on something similar with goals and borrowing the ice hockey stat of Goals Above Replacement. He has written about this and you should read it.

To do this, I first needed to determine the average expected value I was going to use. I also decided to use xG per 90 numbers so minutes played would not skew the numbers. For all “attacking” players (as classified by Transfermarkt) that played at least 400 minutes and took one shot last season, the average xG per 90 was 0.18 in the SPFL Premiership (perhaps tellingly that was the Non-Penalty Goals per 90 minutes for that same group as well).

Now that we have an average, we can compare your favorite player’s xG per 90 with the average. We divide a players xG per 90 by the average and then multiply that by 100. Heart of Midlothian v Glasghow Rangers, 1st, February, 2017This gives us what I am, tentatively, terming as a player’s xG per 90+. If you have an xG per 90+ of 100, you are average, at least when it comes to your xG per 90. Every number above 100 is a 1% better than average, anything number below 100 is 1% below average. Let us use Adam Rooney again as an example, last season Rooney averaged a 0.33 xG per 90. We divide 0.33 by 0.18 and multiply by 100 and we get an xG per 90+ of 140, meaning he was 40% better than the average “attacker” in the SPFL last season. Got it? Ok, let’s look at some numbers and see what we can get from them.

xG p 90+_NPG p 90

The above chart compares attackers from the SPFL Premiership xG per 90+ and Non-Penalty Goals per 90 who have at least played 400 minutes and taken a shot last season, and we see many names we would expect towards the top. Dembele, Griffiths, Sinclair, Moult, Boyce, etc. However there were some things I noticed and first was Esmael Gonclaves, aka Isma of Hearts. The Portugese striker was brought to Tynecastle in January and with the controversy that surrounded the Jambos and manager Ian Cathro in that time, Isma put together some impressive stats during a time when not many impressed in maroon. In fact, Isma had a xG+ of 219.7 (meaning he was 119% better than the average striker in the SPFL last season), which was second among SPFL attackers. Combine that with a very impressive Goals per 90 of 0.41 in his time last season, and one could think that given a full season at Hearts, Isma could be among the top scorers next season with that performance. Of course, all this is dependent on Isma not wringing Ian Cathro’s neck on the touchline, which is not a given.

Something else I noticed on this graph was Alex Fisher. The recent Motherwell signing had a xG per 90+ of 145.6 (or he was 45.6% better than the average attacker last season). He also had the highest Goals per 90 of this group at 0.66, but finishing can be fickle and can fluctuate up and down. However, we see Fisher having similar xG per 90+ numbers as Liam Boyce, Louis Moult, and Adam Rooney. Last year, Fisher only played around 800 minutes for Inverness Caley Thistle. We would expect him to get more playing time next year, seemingly as the replacement for Scott McDonald for the Steelmen. While he may see some regression in goal scoring, if he can continue these good xG per 90+ numbers, we can guess he will be able to add more goals for Motherwell.

In addition to using this metric to compare players and their xG, we can also compare players xA as well. We calculate “xA per 90+” the same we do for xG. We take the league average xA per 90 of 0.13 and divide a player’s xA per 90 by that average and then multiply it by 100. In this chart, we see the xA per 90+ and Assists per 90 for every SPFL Premiership player who has played at least 400 minutes and have one assist.

Dashboard 1

Perhaps surprisingly, we see Leigh Griffiths with the highest xA per 90+. I have discussed Griffiths being more than a goal scorer before and we have more proof here that Griffiths is more than a poacher, with Griffiths xA numbers 212% better than average with an xA per 90+ of 312. We also see Ryan Christie second in this metric, with an xA per 90+ of 229. Numerous people have been very impressed with Christie’s time at Aberdeen last year, and the winger will be back at Pittodrie on loan this season. With such impressive numbers, Christie could be  replacement for Johnny Hayes the Dons are looking for this season.

Another surprising name we see right next to Christie is Motherwell’s Elliot Frear. The English midfielder arrived at Fir Park in late January and put in some impressive frear.jpgperformances to help the Steelmen avoid relegation last season, and his xA per 90+ of 220.8, third highest in the SPFL. With a full season with the ‘Well, if Frear can replicate these numbers, he could put together a great season with Motherwell.

Finally to bring everything around full circle, we again look at Barrie McKay. When we see his xA per 90+ numbers, we see he had a 109 xA per 90+. That means McKay was 9% better than the average player in the league when it comes to xA, which was 17th best in the league. With this new metric, I am hopeful that stats like expected goals and expected assists will become more than abstract concepts and a tool we can use to compare players contributions, like McKay’s, the rest of the league. I am also open to suggestions on the name or any other comments you have about it.

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.

Bringing the Age Curve to the SPFL

The parallels between analytics in football and where analytics in Baseball was years

Photo Courtesy of David Rogers

ago are uncanny. Like stats in football now, analytics in baseball was once considered the domain of nerds. It now has widespread acceptance with both media and teams alike. Michael Caley, who could be compared to a baseball analytics’s Bill James, is a well-respected writer in football stats, known especially for his work with expected goals, but in March he made the parallels between baseball and football stats closer when he applied the idea of an age curve to the “Big 4” football leagues in Europe. A term any listener of “Effectively Wild” (If you made Moussa Dembele run backwards when he played, how many goals would he score?!) would recognize, Caley took the total minutes played in each position by each age and divided that by the total minutes played to see what ages played the most in the big leagues. He found that the “peak years” for players in those leagues were 25-28, though that changed a bit by position. If you haven’t read the article by Michael on ESPN, go here now. It is a great example of seemingly simple analysis that can give huge insights to clubs. I’ll wait while you read.

Back? Cool. One of things that struck me after reading this was its application to Scotland. I believed there was probably a similar trend in the SPFL. However, when the Caley’s article was first published, Kenny Miller had not yet been signed to an extension for next year by Rangers. Miller was Rangers leading scorer and the Glasgow club would likely not be in 3rd place, despite Miller turning 37 this season. Clearly Miller was a huge part of Rangers season but was well outside those peak years. I also thought of my article on Don Cowie leading Hearts in Expected Assists and being among the xA leaders in the league despite being 33 years old this season. Steven McLean scored again against Celtic Saturday and is on pace for another 10 goal season in the league at the age of 34. Of course, there is the fantastic season young Moussa Dembele and Patrick Roberts has had with Celtic, despite being younger than those typical peak years. With players the age Miller, Cowie, Dembele and Roberts in mind, I looked to calculate the same percentage of minutes played by age for the SPFL.

Forward Minutes.jpg

The largest percentage of minutes played by forwards are by 25 years old (17.6%) in the SPFL, similar to what Caley found for the Big 4. However, the next largest percentage of minutes are played by 23 year olds (8.81%) and 29 year olds (11.48%). These ages are playing a much higher percentage of minutes in the SPFL than they are in the biggest leagues in Europe. 22 year olds (7.48%) also play a higher percentage of minutes in the SPFL than anyone aged 23-28 (besides 25 year olds) and play much more than they do in the big leagues of Europe. And of course we see Kenny Miller’s contribution at the end of the graph as a 37 year old.

Midfielder Minutes

We see a very similar story when we look at midfielders. There is a high percentage of 24-28 year olds playing in the SPFL, as there is in big European leagues. However, the largest percentage of minutes in the SPFL played by midfielders are played by 22 year olds (11.71%). 20 year old midfielders are getting plenty of playing time as well (6.54%), but the thing that sticks out to me here is the minutes 30+ year old midfielders are getting. Midfielders at least 30 years old are getting nearly 18% of all the midfield minutes in the SPFL this year, a very sizable chunk.

Clearly, both youngsters looking to make a name for themselves and older players who may not get a chance anymore in a bigger league can find playing time in the SPFL. But what type of contribution are these players outside the typical “peak years” bring to the league?

Shots xG Age

Not only do both younger and older players than the typical peak ages get to play in the SPFL, they do well. Ages 20 to 24 average a higher expected goals per 90 minutes in the SPFL than ages 25 through 29, as well as averaging similar numbers of shots per 90 as those ages. We also see various 30+ year olds averaging very respectable shot and xG per 90 numbers that are higher than we see in the biggest leagues.

xA Age

If we look at expected assist numbers for midfielders, the highest average xA per 90 by age is for 27 year olds. However, that is due to Scott Sinclair, who deservedly won the player of the year award last week. Besides Sinclair, we see high xA numbers for ages 29-33, which were the ages Michael Caley found xA start to drop for the bigger leagues. On the other end of the graph, we again see players younger than the 25-29 year old peak being contributors to their club with good xA numbers.

Brendan Rodgers arrival at Celtic has seen them able to attract players to the club they

Photo Courtesy of SNS

would not have been able to sign the past few years. However, the rest of the SPFL is likely unable to bring in this caliber of player. Yet, those other SPFL clubs can look at data like this and know that they will be able to get more out of a younger player who hasn’t hit his peak or an older player past his traditional peak than a club in a bigger league might get out of him. With this knowledge Scottish clubs can look for younger players who will improve with the minutes an SPFL club can give them and then sell them on for a profit or an older player who they sign for a bargain price and still make a meaningful contribution on the field. While finding players at their peak is a key in England, Spain and Germany, you can exploit an inefficiency in Scotland by avoiding those peak aged players. With data and scouting, your club can find the next Don Cowie or even perhaps the next Moussa Dembele.

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.

What Type of Voodoo is Tommy Wright Working at St. Johnstone?

On Saturday St. Johnstone beat Aberdeen 2-0 despite being outshot 13-8 and the xG being in favor of Aberdeen 1.10-0.66. It was a pretty typical match for St. Johnstone this season as they have a negative xG differential and TSR and TSoR’s below 0.5 meaning they allow

Tommy Wright
“Now go do that voodoo that you do so well!”

more shots and shots on target than they take. Despite this, Tommy Wright has the Saints in the top 6 again, which they have been every year he has been in charge and seemingly destined for Europe with a 4th place. Wright has a 44% winning percentage, only Owen Coyle has a higher percentage at St. Johnstone in much less games than Wright (Coyle was the last manager Transfermarkt had win numbers for). So what exactly is Wright been able to do to have his club finish with less shots than their opponents and a lower expected goals but still be destined for Europa League qualifiers?

Many have credited St. Johnstone’s success to their ability to convert the chances they do get. Goal conversion rate has been a hot topic in football analytics lately. Is finishing ability a skill that differs between players? Will it usually regress to the mean? The godfather of Scottish football analytics, Seth Dobson, did a great, easy to understand article discussing conversion rate in Scotland. In his article, Seth found that it takes usually around 3,000 shots before conversion rate stabilizes in the SPFL. Before that, conversion rate has huge variation, swinging up and down. Seth also found that Celtic has a higher conversion rate than the rest of the league, while the rest of the league has an average conversion rate between 13-15%.

Coversion Rate Graph SPFL
Image Courtesy of Seth Dobson,

With that 13-15% average range in mind, let us look at St. Johnstone’s conversion rate since Tommy Wright has been at the club. As we see in the graph below, St. Johnstone had a goal conversion rate of 13.26% in 2013/2014, 11.85% in 2014/2015, 16.57% in 2015/2016, and 13.86% in 2016/2017. This works out to an average 13.97%. We see their conversion rate slip below average in 13/14 but then it rise above average in 15/16. So it does not seem that St. Johnstone are any better or worse than the average SPFL club at finishing chances in Tommy Wright’s time at the club.

St. J Conversion Rate.png

Furthermore, their expected goal numbers this year are not anything to write home about. As I previously mentioned, they have a negative xG difference so far this year. They are 7th in xG per game and 9th in xG per shot. It does not seem that the success Tommy Wright has had is due to St. Johnstone’s attack.

Dashboard 1-6

If it is not St. Johnstone’s attack that is leading them to success, than surely it has got to be their defense that is leading them to success. Well, only Dundee and Motherwell have conceded more shots than St. Johnstone have this year. Yet, St. Johnstone have the lowest Conceded Conversion Rate in the SPFL this year at 9.83%. We know not every shot is equal and we can measure shot quality with expected goals. Though when we look at their xG conceded numbers we see they are 7th in the league xG against, which isn’t awful but isn’t great either. However, when we look at St. Johnstone’s xG Against per shot, we see only Partick Thistle, Celtic, and Rangers have a lower xG per shot allowed. St. Johnstone are allowing a lot of shots, but many are low quality chances.

Dashboard 2.png

Exploring the type of chances the Saints defense allows further, we can look at the number of danger zone shots they allow. The average number of shots allowed in the danger zone (the area in the 18 yard box in between the 6 yard box) in the SPFL this year is 45.62%. However, only 41.63% of the shots that St. Johnstone have allowed have come from the danger zone. Another sign that while St. Johnstone allows a high number of shots, they are lower quality shots than the rest of the league typically allows.

St Johnstone Shots Conceded Locations.png
Locations of Shots St. Johnstone have Allowed This Season

So we see St. Johnstone have been particularly good at limiting opponents to worse shots than much of the rest of the league. In addition to providing me with data that hasn’t been available for Scottish football publicly, the good folks at Strata have also attempted to quantify the defensive pressure a shot faces when taken. Using a scale of 0-5, they base the defensive pressure ranking as follows:

0 – No defensive players around, nobody blocking the shot

1 – Light defensive pressure, no direct tackle but a player stood a few yards away causing some part of the goal to be blocked

2 – Low defensive pressure, a player a few yard away but could be sticking a leg out looking to make a block

3 – Medium defensive pressure – Close contact with a defender, a player blocking the ball from close range, s player holding onto the shirt but behind the man

4 – High defensive pressure – Many defenders crowding around the shooting player, Tackles being made as the shot is taken, very close contact when jumping to meet a header

5 – Intense defensive pressure – a player being held while taking a shot, many players all making tackles together giving very little room for a strike, a player crowded out when challenging for a header

With those defensive pressure rankings, I found the average shot in the SPFL this season has a defensive pressure of 2.04. However, St. Johnstone have an average defensive pressure of 2.14, meaning they are typically getting more pressure on shots taken than the average SPFL shot, further evidence that the Saints are allowing a lower quality shot than much of the rest of the Scottish Premiership.

Tommy Wright clearly has made an organized defense the heart of his St. Johnstone team. The numbers point to another organized back line that keeps it’s shape to prevent opponents from taking quality shots and those shots they do allow have plenty of defensive pressure being applied to them. Wright has the Saints organized and you would have to wonder how long until a bigger club either here in Scotland or elsewhere notices his success with St. Johnstone and want him to bring the same defensive organization there. Until then, Saints supporters will enjoy the continued success Wright has brought their club.

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.


The Biggest Underachievers in the SPFL: Tony Watt said what?!

Being petty is something I believe I have shown I have no problem doing. After namingWatt Yellow.jpg my SPFL projection model after “He’s probably compensating for something” Proper Football Man Craig Burley, my credentials in the petty game would hopefully be unquestioned. So what has set off my petty radar today? It is none other than Tony “Hey, did you know I scored against Barcelona?” Watt.

I was minding my own business, scrolling on Twitter when Craig Fowler, who I think has been doing a great job covering Scottish football with his colleagues at The Terrace and the Scotsman, posted an article chronicling five “hot takes” people who have been invited to promote the Scottish Cup this season have spouted. In February, after being sent home from loan early from Hearts for having another disappointing season in his disappointing career, Watt was strangely asked to perform this PR exercise. From there, Tony gleefully danced with a lit torch and gasoline as he burned his bridges in Scottish football. Craig in his article says:

“There was a reason Hearts never let the striker speak to the media either before or after matches during his six-month spell in Gorgie, and it quickly became apparent when he was invited back north to promote the Scottish Cup fifth-round in February. Instead of talking up the product, Watt said Scottish football was “not for me” and that he didn’t see himself in the SPFL for “the next five or ten years”. “Everything” about English football was better.”

Now, more illustrious football luminaries have made similar comments. These are nothing new and most football fans in Scotland are usually able to brush them off. I vaguely remember hearing these comments and thinking “Good luck in League Two in England next year Tony,” and went on with my day. However, a perfect storm of Craig posting that article bringing those comments back into my focus and updating the SPFL Premiership stats from the last round of fixtures put me on the stats warpath.

Along with the usual stats I publish, I have been calculating the Goals subtracted by Expected Goals for each player in the SPFL. The thought with this number is that you can see who is under-achieving and over-achieving on attack. If a player has a positive number with the Goals-xG, they are “overachieving”. From this we could gather that Azeez cautioneither they are due for some regression in their performance or if they are able to continually over achieve, you could be looking at a potential superstar player (for example, Leo Messi consistently outperforms his xG numbers).

On the other end, players with the lowest negative Goals-xG have been underachieving. Similar to those overachieving, if a player has a negative G-xG they are candidates to possibly see an improved performance as a season continues. The other option is they are just not a good player, constantly flubbing quality scoring opportunities. After looking at these numbers and finding the biggest underachiever in the league, can you guess who it is? Yes, it is our old friend Anthony “Barca” Watt.

Screen Shot 2017-04-11 at 8.27.38 PM.png

Despite not playing in the league for months, Tony Watt underperformed his expected goals more than any player in the league by nearly a full goal. His xG total suggests he should have scored 4-5 more goals in his time in Scotland, you know 4-5 more than the 1 single league goal he scored.

Now of course, there are players on that underperforming list that are still in the league and haven’t compounded their lack of success by saying Scotland was “not for them”. I guess, Belgium, Wales, and England aren’t for you either, eh Tone? Regardless, we see
Adebayo Azeez second in the league in underperformance. However, we already saw luck bounce a bit in Azeez’s direction when he scored Partick Thistle’s equalizer at Celtic Park last week to earn the Jags a 1-1 draw. Though I applaud his gaul to be both the biggest underachiever in the SPFL not named Tony Watt this season and earn a yellow card for excessive celebration when he finally does score.

Sheet 1-4

We also see four Rangers players on the list of the 10 biggest players underperforming in the SPFL. James Tavernier, while very attack minded, is a defender, so we cannot fault him too much for not being on this list. Jason Holt mostly plays central midfield, a position we wouldn’t always expect a high number of goals. But many of Rangers issues Garner Yellowthis season are very much linked with the fact that Martyn Waghorn and Joe Garner are on this list, each having expected to score between 3-4 more goals each this season based on their xG. Whether or not Rangers re-sign Kenny Miller, a striker should be a priority this off-season.

Of course, Kenny McLean appears on this last after I write 1000 words about how his improved performance has helped Aberdeen move into second. I would like remind that I predicted McLean to start scoring more (and he has scored since), as well as that it’s McLean’s forward position and passing that has really helped the Dons. Also on the list is Tom Hateley of Dundee. Hateley has been a player that has caused great frustration for Dee supporters. He has good xA numbers and key pass stats, but has clearly struggled also adding goals to his game this year. Dundee will need that to change as they very much find themselves in a relegation fight.

So in conclusion, perhaps if the thing you have known for most in your career is underachieving and not meeting your potential, maybe you shouldn’t talk about being better than the league you are leaving after you spent months continuing to underachieve. Of course, the applications of using Goals-Expected Goals goes beyond pettiness. A player could be in a run of bad luck and about to turn it around, or just not be what your team needs. Using this with other metrics and video analysis can help you make that determination. Though sometimes being petty is more fun.

Unleashing Kenny McLean: Aberdeen Push Up Midfielder to Move Up the Table

No, YOU’RE great Kenny!

On December 17th, 2016 Aberdeen had just lost 2-1 to Ross County at Victoria Park. The Dons had a lackluster performance that saw them have a lower expected goals total than the Staggies. The lost saw them go to seven points behind Rangers for second place, albeit with a game in hand. Since that loss in the Highlands, Aberdeen has gone on a tremendous run, winning 10 matches and losing only 2. Even the matches they lost were against run-away champions elect Celtic and to Hamilton in a match they had a 2.17 to 0.34 xG advantage.

Aberdeen Team Stats


From the beginning of the season to December 17th, Aberdeen averaged an xG of 1.42 for, but a 2.25 xG per game after 12/17. They averaged 10.65 shots a game before 12/17 and 15.25 shots a game after 12/17.So what has caused Aberdeen’s great run to move into second place over Rangers and the rest of the league with some breathing room? Clearly something has changed at Aberdeen and after looking at individual numbers, it seems that Kenny McClean has been the catalyst to this run for the Dons. 

McLean Stats

When looking at McLean’s numbers between the two halves of Aberdeen’s season, it is clear that McLean’s influence was much greater after December when Aberdeen found their most recent form. While McLean scored 2 goals before December 17th compared to none since, in every other attacking stat we see Kenny McLean’s performance metrics improve.

McLean Shots.jpg
McLean’s Shots Before and After 12/17

The finishing fairy has been a bit hot and cold with McLean, as he scored 2 non-penalty goals before December 17th but had an expected goal total of 1 and xG per 90 minutes of 0.06. After the 17th, the midfielder has 4.4 xG and a 0.35 xG per 90. While he has not scored since November, if McLean can continue to put these xG numbers he will likely be among the goals again soon.

McLean Passes
McLean’s Key Passes Before and After 12/17

While Kenny McLean has not contributed goals to Aberdeen’s recent run, he certainly has been a playmaker for the Dons lately. McLean has contributed 2 assists to the Reds since December 17th, where he had 0 before that. When looking at the Expected Assists stat I discussed last month, before the 17 McLean had a rather meager 0.71 xA and 0.04 xA per 90 compared to a 2.02 xA and 0.16 xA per 90 after December 17th. With Key Passes, or passes that directly lead to a shot, McLean increased the number of key passes he made from 6 before 12/17 to 15 after. This is a jump from 0.35 to 1.20 Key Passes per 90 minutes. He also has had 6 Key Passes leading to Danger Zone shots after 12/17, compared to 4 from 12/17 and before. McLean has made his influence felt for Aberdeen and it seems that the Dons have benefitted from his growing influence on the pitch.

McLean Assisted Shots
McLean’s Key Passes Leading to Danger Zone Shots Before and After 12/17

So what has changed since that December 17th game against Ross County with McLean? It seems that Derek McInnes has been deploying McLean in a more advanced role. There was no doubt James Maddison was very talented and important to Aberdeen the first half of the season, but once he went back to Norwich City the opportunity opened up for McLean to take up the advanced position Maddison was deployed in. I asked Aberdeen supporter and early friend of the blog Scott Burness if he thought McLean was playing higher up and he said:

“McLean has indeed been playing a more advanced role. On Saturday (March 18th) v Hearts he was playing more advanced than a typical 10. Think that was simply a tactical move. Recently there’s been less need to chase games second half (other than Hamilton McLean 2and Motherwell) and as such Shinnie has been in midfield rather than left back. Normally when [Aberdeen] chase games, Shinnie drops to LB McLean fills his role in CM. That said, when chasing games [Aberdeen have] gone to 3 at the back allowing McLean to stay further forward. Maddison not coming back has been a blessing in disguise as that has allowed McLean to flourish.”

There has been controversy surrounding McLean not getting a call up to the latest Scotland squad, furthered only after Scotland’s uninspiring performance in their friendly versus Canada. The Dons midfielder can feel slightly aggrieved not receiving a Scotland cap after this improved form and helping to lead Aberdeen to this great run. However, not getting Scotland caps will not be a problem if McLean can continue this form. If Gordon Strachan is to remain as manager of Scotland, most are calling for him to start to look towards the future with the Scotland squads he will be calling up and McLean will be a part of the those squads in the future with this form.

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.

Finding the Best Pass in the SPFL (Don’t Look at Rugby Park)

The image here is not one of those ink-blot pictures that psychiatrists show you. Rather, it is SPFL Passesshowing where the passer was located for every shot in the SPFL Premiership through February 25th. 2976 passes and 2976 shots (and each one of them mattered damn it, despite what some Times writer in England may think). I previously discussed what we could learn about individual players in the SPFL by their passing thanks to the data that Strata provided me. With that data we got Expected Assists and confirmed Scott Sinclair’s great season while learning how important Don Cowie and James Tavernier are to their clubs attack. Now we will see what we can glean from this same data on a macro level league wide looking at Key Passes, or every pass that has lead to a shot in the SPFL.

Image by The Rangers Report @TheGersReport

Before heading into the secret world of the private sector, The Rangers Report did some great work looking where passes came from and why that mattered. The Report would track the starting location of each pass that lead to a shot for Rangers, also known as a Key Pass and then placed them into numerical zones on the pitch (based on the methods of Claude Moeller Henriksen). However, since the fine folks at Stratabet have provided me all of locations of passers for these 2,900+ shots, I don’t have to spend hours tediously tracking these locations. With this data and using the same zones Rangers Report came up with, I thought it would be interesting to see just what passing locations lead to the most success in the SPFL Premiership this year.

SPFL Pass_Score Data

Looking at the data, it becomes clear that the closer to the goal line and more central you are when you make your pass the higher the chance is that your teammate will score on their subsequent shot. We see passes from zone 22, or the 6 yard box, lead to goal 57% of the time. Zones 17 and 19 lead to goals over 23% of the time, with a left zone 17 pass RangersReportPassingZonesleading to a goal nearly 30% of the time. If you make a pass anywhere outside the box, your team is nearly 10% less likely to score than inside the box this season.

Of course, if exploring expected goals and goal conversion rates this year has taught us anything, it is that sometimes we can make a great pass and the striker will inexplicably miss. There has been numerous work discussing that shots in the “danger zone”, or shots in the 18 yard box between the goal posts, have the highest probability for going in. Those are the shots you want your team taking. Therefore, you would also want your team making the passes that would most lead to those shots.

To do this we can look of the percentage of key passes that result of danger zone shots over all of the key passes. Again we see passes being made in zones 22, 17 and 19 leading to the most danger zone shots. We also see passes from zone 15, outside the box but still within 18 yards of the goal line, leading to a high number of

Krys Boyd loves long balls and hates laptops

danger zone shots. Regardless of finishing, making a pass within 18 yards of the goal is the best way to get a good shot for your team in the SPFL.

So most would agree that these numbers would suggest the death of “route one” football. If you want to be among the goals, your final key pass will need to be in and around the box more often than not. Since we have all of this passing data, we can see which teams are taking the most key passes from farther out(~30 yards or farther). The clubs who take the most passes outside the box would probably be on the bottom of the table right? Well, as we see Celtic with the most key passes from 18 yards out, but that is due to the fact that Celtic has the most shots by a wide margin in the league and thus has the most key passes in all of the various passing zones, not just from long distance.

SPFL Long Ball Table

So instead of total number of passes, the percentage of long balls compared to total key passes each team plays might be more telling of a playing style for a club. And indeed we see bottom half clubs Kilmarnock and Inverness CT playing the highest percentage of key passes as long balls. Bottom six clubs Dundee and Hamilton round out the top 4 of highest percentage of long balls for their key passes. It certainly seems those relying on the long ball is not a way to be successful in the SPFL.

Killie Key Passes
Kilmarnock Pass Map: This is a bad SPFL team

This data has lots of possibilities. You can narrow it down to the team level to see where teams are most passing to lead to goals and shots. You can compare multiple clubs styles. You can do all of this with individual players. A club with this data and the right interpretation can get a leg up on a club who does not in the SPFL.

Celtic Key Passes
Celtic Pass Map: This is a good SPFL team

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.

Introducing Expected Assists to Scottish Football: Sinclair, Tavernier, and Cowie Stir their Team’s Drink

The idea of “expected assists” (or xA for short) has been in football for awhile now. However, like most statistical innovations in football, it has yet to be publicly available forSinclair.jpg Scottish football. This was incredibly frustrating for me, as there were players that clearly contributing to their team’s attack but it was not showing in something like expected goals since they were not necessarily shooting as much as other players. Expected assists helps to quantify this.

Similar to its more famous cousin expected goals, expected assists quantifies chance quality, but it counts the quality of the pass that led to a shot. As football stats guru Michael Caley states, “Of course, assists can mislead. A player might get lucky a few times that he happened to make the pass before his teammate took an incredible shot. To be confident that a player with big assist numbers is in fact pulling the strings requires a bit more statistical evidence.” Also like xG to goals, xA is a better measure of a player’s passing ability than regular assists are. An important part of thinking about football stats is considering if it makes sense in the sense of the game. A pass is still a great pass whether the person shooting is able to finish the shot for a goal or send it into Row Z while falling on their butt and becoming a viral video. Expected assists counts all of these passes and what type of shot they lead to, regardless of whether that conclusion is a goal or not.

Expected assists is the stat that can give us that insight into those string pullers for a club, but up until now information on who was providing the passes that your favorite team’s crap striker was missing was unavailable publicly for Scottish football. Recently though, the allcowie around good looking folks at Stratabet have provided me with the data necessary to start calculating xA for the SPFL Premiership. Along with providing me data, I have been a subscriber to Stratabet’s weekly emails for awhile now. While I cannot use their betting tips since “Making America Great Again” apparently does not include repealing archaic gambling laws, their emails have had plenty of interesting stats related articles on topics such as free kicks, finishing skill, and more that if you are interesting in stats in football will more than tickle your fancy.

Using my expected goals model and the data Stratabet has provided me, I can now include xA in my weekly stats updates and leaders in the SPFL Premiership. Having to come up with the xA stats for the previous 20+ games in the league has been a bit time consuming, but I have been able to get the numbers done for the Top 4 in the SPFL table thus far. As I’m sure we all expected, the player with the highest xA on either Celtic, Aberdeen, Rangers, and Hearts is…Don Cowie?!?!? Ok, so maybe he is not the high profile name some would expect to be on top of the league in xA. Yet, Cowie has played the highest number of minutes in the league for Hearts, who have the highest goals scored and xG for by any club not named Celtic in Scotland. Can you imagine how many assists Cowie would have if he didn’t have to pass it to Tony Watt and Connor Sammon for much of the year?!


Rounding out the players with the 10 best xA totals thus far from Celtic, Aberdeen, Rangers and Hearts (and perhaps you could assume they would be the top 10 of the league given the gulf between the top and bottom of the SPFL Premiership, but you know what they say about assuming) we see James Tavernier, Scott Sinclair, Jamie Walker, Niall McGinn, Johnny Hayes, Stuart Armstrong, Leigh Griffiths, James Maddison, and Barrie McKay. If we combine both xG and xA, we can see just which players among the top 4 are offering the most for their side on the attack.


While fans and pundits alike have deservingly praised Moussa Dembele for his incredible form this season, Scott Sinclair has the highest xG+xA total among the top 4 of the SPFL so far this season. Sinclair has both set up his teammates and scoring goals on his own for Celtic, leading in both xG+xA as well as the top goal scorer in the league and joint top assists. Dembele may be the subject of mega-money transfer deals, but you could make a very convincing argument that it is Scott Sinclair who has been Celtic’s best player as they have run away with the SPFL title this season.

xG+xA p 90_G+A p 90.png

Can we spare a thought for James Tavernier at Rangers this season? There has certainly been a lot going on at Ibrox in the past few weeks. If you do not bring into account the whole Mark Warburton saga over the past few days, Tavernier seems to be the forgotten man behind “£6,000,000 man” Barrie McKay. However, Tavernier has the 2nd highest xAtavernier in the league, the 6th highest xG+xA in the league and highest at Rangers. Before the season it seemed that Tavernier would be the Rangers player destined for a high transfer fee, but that seemingly has been forgotten. Amongst the circus at Ibrox this year, Tavernier has been putting together another solid season for Rangers. He only has 1 goal and 2 assists, but has had to create much of the Rangers attack this season. His assists totals are not helped by the lack of talent Rangers have had at striker. If Tavernier had a more clinical striker (like say, oh Liam Boyce), his assist numbers would be higher.

Once I finish going through all the old matches, I will have xA and xG+xA leaders for the SPFL up with the rest of the stats. Thanks again to Stratabet for providing me with this data. This has been something I wanted to bring to SPFL supporters for awhile and they made it happen. I also have some plans for some more fun things to do with the data they are providing me.

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.