Scottish Football Graduating to “Advanced” Expected Goals

Congratulations Scotland, you have passed Intro to Expected Goals and are now movingProfessor.jpg onto the advanced class. Most following me know that expected goals are the likelihood that a goal will be scored on a shot. Expected goals is now a term that more and more Scottish football fans are familiar with, understand, and can discuss coherently. Sure, there is the occasional “Yer Da” still yelling about “Goals and Points being the only stat that matters!”, but compared to three years ago, football analytics literacy has grown considerably in Scotland.

However, now that many have the basics down, we need to have a talk about expected goals. On Twitter last week, I noticed there was discussion about the usage of xG and in particular summing xG totals for individual matches and saying things like “(this team) should have scored 2 goals because they had an xG of 2.” Let me first throw myself at the mercy of the metaphorical court, I have created a few different visualizations where a summed xG total for an individual match was present. It is still on the xG maps I publish each week for the SPFL.

I chose to sum xG on the graphics I have posted to try and ease Scottish football fans into xG. With that being said, there are some issues with summing xG for individual matches. Danny Page covers the issues in an article he wrote pretty comprehensively. Danny points out that if you sum the xG, you will miss on on the variance that can occur in a single match. In his article, he says:

Arsenal won 0–3 with a xG scoreline of 0.39–1.49. In these cases, some may say “The right team won” because the xG and real life scorelines match. However, these values are only adding expected goals. But something is missing. Only adding independent probabilities misses half of the story: variance.

A good situation to think of here is a shot with an xG of 0.05. That shot may go in, it has gone in before, but it is not likely. The instances where it does go in is the variance Danny is talking about, but generally it is not a shot that is going to lead to goals often. But let’s say that a team has ten of those 0.05 xG shots, compared to a team that has one 0.50 xG shot. The second team’s shot is much more likely to go in than any of the first team’s shots, but summing the xG in this situation they would both have an xG total of 0.50.

Ross County Motherwell Prob 11_4_17
The xG graphic that will be out each week for match, borrowing heavily from Danny Page and his xG simulator.

Sometimes those lower xG shots will lead to a win, thus the idea of variance. Typically  summing xG over the course of a season variance usually will find the mean. However, anything can happen in one game. Therefore, Danny puts forth that rather than summing xG totals in a single match and making conclusions off that, it would be better to use win percentages based on the xG of each team’s shots and the likelihood of the goal difference for that match based on the xG output, so that is what we are going to do.

 

To do this, we will take the xG of each shot for a team in a match and run them in a Monte Carlo simulation 1000 times. This is similar to what I do to come up with the numbers for B.U.R.L.E.Y. for the season. With these simulations, we can come up with 1,000 results of matches with the xG results of a particular match and produce how many times each team would typically win and draw, what would be the most common scoreline, and the typical points per game from that xG performance. In addition to seeing the sum of the xG for a match, we will see the team that was most likely to win and what the score would typically be from a match with that xG output.

St. Johnstone Celtic Prob 11_4_17.png
My xG graphic for St. Johnstone v. Celtic on November 4th.

Using Danny’s xG simulator and taking all the graphics he came up with as a template, I will now be producing these graphics for every SPFL match. Henceforth, these graphics will accompany the xG maps we have been producing each week and will hopefully give some further insight into expected goals. As this now the “advanced class”, please feel free to let me know if you have questions or comments about this!

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.

Team Effort the Reason for Motherwell’s Fast Start

The past few years have been a bit of a roller coaster for Motherwell Football Club. Over the last five years, Motherwell have finished ninth, fifth, eleventh, second, and second.Edinburgh City v Motherwell, Betfred Cup Group F, 25 July 2017 While the years of European qualifiers are not that far off, the results at Fir Park have not been as successful lately, including a relegation fright in 2015 when they finished in the relegation playoff spot and had to beat Rangers to keep their Premiership status.

However, this year has started off much differently for Motherwell. While a second place finish similar to the years Stuart McCall led the club might be a bit too much to ask, the Steelmen sit in fifth place in the SPFL Premiership as of writing and have a League Cup final to look forward to. A top six finish would seem to be the expectation now this season for Motherwell. While a good spot in the table is always nice, we know that sometimes the table lies to us and we need to look deeper into the corresponding numbers behind a team’s performance.

SPFL League xG Difference_Goal Difference.png

There is good news when we look at the underlying numbers for Motherwell thus far, we see the stats match their good start. Motherwell have an xG of 13.45 and xG against of 9.30 for a xG difference of 4.15, which is 5th best in the league through 11 matches. These underlying numbers suggest that if the Steelmen can continue that type of performance, they can not only hope for a top six position in the table, but also start to possibly dream about a European adventure again.

It has taken a few years for it to happen, but Louis Moult has finally become a household name among football fans in Scotland. For TheTwoPointOne towards the beginning of this season, I wrote about Moult’s great season last year, but Motherwell needing to find some help on the attack after losing partner Scott McDonald this offseason. Last season, Moult helped carry Motherewell, putting up 15 goals and an xG of 10.74 or 0.35 per 90. This year he is back to putting up good numbers again this season, with 5 goals (3 non-penalty goals) and a non-penalty xG of 3.63, which is 5th in the league.

Moult Bowman.png

Moult seems to be in good form again this season, but he is getting some help from his teammates and that seems to be making all the difference for Motherwell. Before the season started, there were questions who would be taking McDonald’s place as the other Motherwell striker, but Ryan Bowman has stepped up to that role in the first part of the season.

Bowman has matched Moult’s 3 non-penalty goals, with a xG of 2.24 and 0.26 per 90 thus far. While Moult gets lots of shots, ranking 2nd in the league in shots overall and 3rd in shots per 90, Bowman has been able to get good quality shots despite not having the same quantity of shots as Moult, averaging 0.19 xG per shot, which is 7th best in the SPFL, and 66.67% of his shots occur in the danger zone. Will Bowman be able to continue to keep scoring off of such good quality shots as defenses in the league realize they need to account for him? Motherwell’s success may hinge largely on it.

Cadden

Both Moult and Bowman have surely enjoyed the play of young Chris Cadden on the wing for Motherwell. Cadden just recently received a well deserved call up to the Scotland U21 squad (and I contest perhaps should have been called up to the main squad) and has been impressive so far this season. He has 21 key passes so far (2.1 per 90) and a xA of 3.06 (0.2 per 90), which are both 3rd in the league this season. Compare this to the Motherwell squad last season, where no one at Fir Park finished among the top 20 in the SPFL in xA or key passes.

Cadden has only 1 assist this season, which might hint his teammates have let him down a bit, but his passing creativity is clear to see in the numbers and on the pitch. Not only are his key pass and xA numbers impressive, Cadden also has an xSA (expected secondary assist) of 1.11, which is 19th in the league. Cadden has been vital to Motherwell’s potent attack this season and the Steelmen will be relying on him to continue this the remainder of the season.

While the likes of Ryan Bowman and Chris Cadden have helped Louis Moult and Motherwell put out a strong attack on the Fir Park pitch, last season the club actually had a decent attack as well. In 2016, the ‘Well had the 5th highest xG at 56.09. What was not decent for them was their defense, ranking dead last in the SPFL in xG conceded at 74.34.

Key Entries Against_xG Against.png

This year has been a much different story for the Motherwell defense though. Through 11 matches, the club has the second lowest xG against in the SPFL at 9.30. Only Celtic has allowed a lower xG against thus far, as in the team that has not lost in 62 domestic matches, so pretty impressive company. If we look at the number of Key Entries allowed, or any ball that is played beyond the 18 yard line by the opposition while defending, Motherwell has allowed 222 so far, with only Celtic, Hibs, Aberdeen, and Rangers allowing less. Comparing the key entries against to xG against in the SPFL this season, we see a decent relationship, so this number will be interesting to keep track of as the season unfolds.

With such poor numbers last season, Motherwell clearly had to make a change in the back. Enter Cedric Kipre, Charles Dunne, and Peter Hartley. Stephen Robinson has deployed theseKipre.jpg three either in a back three or part of a back five in recent matches and the results speak for themselves. Both Kipre and Dunne were brought to the club on free transfers, while Hartley was brought on loan from Blackpool, though his contract expires in May of 2018. Dunne and Kipre are younger players at 24 and 20 respectively, while Hartley has more experience at age 29. These additions have turned Motherwell from the worst defense last season to one of the best this season. Furthermore, they show that SPFL teams can help turn around part of their team with some shrewd signings.

Last season, Motherwell heavily relied on Louis Moult. Their defense was a shambles and besides Scott McDonald, the attacking burden fell mostly on Moult. But so far this year, it has been much more of a team effort at Fir Park. The club no longer has to rely on Moult to win. If they can continue to get these same types of contributions from Bowman, Cadden, Kipre, Dunne, and Hartley, a top six finish should be the minimum expectation for Motherwell.

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.

A Statistical Preview of Celtic’s Champions League Opponent RSC Anderlecht

Going into match day 2, Celtic could not have a more different Champions League opponent in RSC Anderlecht that match day 1 foe PSG. Like Celtic, Anderlecht were seen as the also rans in the group also featuring mega-bucks squads Bayern Munich and Paris Saint Germain. It was all but assumed Celtic and Anderlecht would be fighting for third at the end of the group, and Anderlecht also was on the wrong end of a big loss (though not as big as of a loss as Celtic) to Bayern Munich in their first Champions League match of the campaign.

Unlike Celtic, things have not gone so well for Anderlecht domestically back in Belgium. onyekuru.jpegThrough eight matches, the reigning Jupiler Pro League champs sit in seventh place, 9 points behind Club Brugge. While this position may spell curtains for teams in other leagues, the Belgian set up allows for some hope. The top 6 teams in the league advance to a playoff. In the playoff, each team has their point total halved and then they play each of the top six teams twice more. The club with the most points at the end of that is the Champion.

While the unique Belgian league set up allows for a team to have hope after a slow start, it was not enough for the powers that be at Anderlecht and after seven matches manager Rene Weiler was sacked. Weiler led the club to the title in his first season last year, but only picking up 2 wins in those first seven matches was not enough for those at the club. While the club struggled to get results under Weiler this season, what do the underlying statistics say about Anderlecht’s performance thus far? Luckily, the fine folks at Strata can help us find out (as well as teach me that there is a club in the Jupiler Pro League that has “excel” in it’s name, a stat nerd’s dream team!)

Sheet 2-2

Looking at the numbers for the Juplier Pro League, we see stats telling a different story than points in the table are telling us for Anderlecht. In fact, Anderlecht’s underlying stats have been fairly good so far this season. Anderlecht has the best xG difference, the 2nd best xG for, and the best xG against through seven matches in the league. The Purple and White have conceded the lowest number of shots and have the highest xG ratio in the league (the ratio of xG for/xG+xG against). The only numbers that are not so impressive for the club is their goal total at 8, their goal difference at -1, and their points at 9.

Dashboard 1-3

These numbers suggest that Anderlecht could be due for some positive regression to the mean, in which their performances start to result in more goals and points on the table. They also suggest maybe the club should have been more patient with Weiler, as the performances were OK despite Anderlecht being on the receiving end of some bad luck. With Nicolas Frutos, David Sesa and Thomas Binggeli are taking over managing the club on an interim basis, will the club see that positive regression that the stats say their performances deserve or will the sacking throw the club into disarray and see performances worsen? Hopefully for Celtic, it will be the latter.

Anderlecht Goals.png

Taking a look at where Anderlecht’s goals have come from domestically, a pretty clear pattern emerges. Five of Anderlecht’s six league goals in their first seven matches came from the area we like to call the danger zone, where you are more likely to score. On the other end of the ball, seven of nine of their goals conceded came from the danger zone they were defending. Furthermore, five of nine came from crosses, but only one from a header. To me, this screams out a weakness that Kieran Tierney can exploit. Tierney is wonderful at bombing down the wings and making a killer cut back low cross. This sets up his teammates for this high probability danger zone shots. Seeing as this seems to be something Anderlecht is susceptible to and is a strength of Celtic’s, one would think this is something Celtic will need to exploit to be successful Wednesday.

xG Leaders Belgium.png

If we look at some individual players Celtic will need to look out for, a few names pop up on the Anderlecht squad list. Henry Onyekuru and Lukasz Teodorczyk are tops at the club in goals and expected goals, and seventh and eighth in xG in the league respectively. Everton loanee Onyekuru, who says he turned down a move to Celtic this summer, has 3 goals, an xG of 2.75, 0.61 xG per 90, and 3.3 xGAS, while Teodorczyk, the £4m signing from Dynamo Kyiv, has an xG of 2.59, 0.44 xG per 90, and a 4.26 xGAS. These two have been Anderlecht’s most high potent attackers domestically, though neither started their last match against Waasland-Beveren, though Onyekuru came on as a sub at half.

xSA Belgium.png

Looking at some xA numbers, Teodorczyk has the highest expected assist numbers at the club and is twelfth in the league at 1.52. The man who is pulling the strings for the Anderlecht attack has to be Sofiane Hanni. Hanni has the highest expected secondary assist total at the club and is second highest xSA total in the league at 1.87. Hanni isHanni usually deployed as an attacking midfielder centrally and is the linchpin to the Anderlecht attack that has yet to find the goals, but has underlying numbers that suggest they are on the verge of being unleashed.

While it is hard to speak about the Belgian press, even the most ardent Celtic supporter probably understands it is a race for 3rd place after the 5-0 loss to Paris St. Germain in match day 1. To finish third and see European football past Christmas, Celtic probably need at a minimum of four points from their two matches with Anderlecht. The Purple and White may have struggled domestically thus far, but their underlying numbers suggest their is a good squad in there. Will Anderlecht rebound after Rene Weiler’s sacking? It is hard to guess, but Celtic would be wise not to take them for granted.

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.

Alfredo Morelos: Some Quick Praise from a Small Sample

I can assure you there will no tired/borderline racist cliches in this article about Alfredo Morelos. Last season, Rangers had the second highest expected goal total in the SPFL, yet finished third in the table. Whether that is due to bad luck or poor finishing can be debated, but as a result Pedro Caixinha and Rangers decided that they needed to upgrade at striker. Out went Martyn Waghorn and Joe Garner and in came Alfredo Morelos from HJK Helsinki. Last season, Morelos scored 20 goals in all competitions for the Finnish club, but could he continue to score goals in the step up to the SPFL?

Morelos.png

Through six games, Morelos has certainly answered that question with a resounding “yes”. Going into the match-up with Celtic, the Colombian is currently top the goal scoring table in the SPFL Premiership with 6 goals. In addition to having the most goals in the league, Morelos has the highest xG in our model at 3.61, which is 0.77 per 90 minutes and 0.23 per attempt.

Looking at these numbers and his shot map above, it is easy to see why Morelos has had early success with Rangers. the Colombian striker has been able to get into great positions and get off shots with a higher than average scoring probability. 63% of Morelos’ shots this season have come from the Danger Zone, or the area in the box between the ends of the 6-yard box. All but one of his shots have been inside the box thus far. Morelos clearly thrives inside the box, getting shots where they can do the most damage.

SPFL xG leaders

If we compare Morelos’ per 90 numbers for xG to Moussa Dembele’s from last season (0.69 xG per 90), they are similar. Of course, it is way too early to declare Morelos on the same level as Dembele, but if the Colombian striker can continue his output at this pace it would not be unreasonable to compare the two. However, even if Morelos can reach the same rarefied air that Dembele did last season, Rangers still may need someone else to help ease the burden on “El Bufalo”.

Besides Morelos, Josh Windass and Kenny Miller are both among the top 20 xG leaders in the SPFL that are on Rangers. Yet, between Miller and Windass, they haveMorelos 3 1 league goal between them. Morelos may have the talent to carry Rangers for a period of time, but like all players, he could find himself out of favor with the finishing fairy and not find the net for a few matches. If Celtic are without Moussa Dembele, as they have been up until recently, they have the likes of Scott Sinclair and Leigh Griffiths that can help knock in goals. Can Morelos’ Rangers teammates provide the same support? It has yet to be seen.

While six games is a small sample in which we do not want to jump to conclusions, it certainly seems Rangers have upgraded significantly in the striker position from last year with Morelos. With such fine play, the Colombian striker is already subject to transfer rumors. However, if Rangers can hold onto him all season, they can expect to see greatly improved fortunes in front of the goal from last season.

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.

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, Goal.com, 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

Dembers
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).

Dembele

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.

xG RBK

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.

xGAS RBK

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

Miller
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

Cowie
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, https://twitter.com/226blog

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.