Aubameyang vs. Giroud

In every transfer window there are lots of rumors about player swaps. In the end, most of these rumors remain unfulfilled since player swaps are a rare thing to happen. The more surprising it is, that Arsenal is aiming to complete two player swaps in this transfer window, in which four high-quality players are involved. They have already swapped Alexis Sánchez with Henrikh Mkhitaryan and are now targeting to sign Pierre-Emerick Aubameyang and send Olivier Giroud to Borussia Dortmund. A great reason for Footballelixir to illuminate the qualities of both players and look at their development over the past few years. Let us start with Pierre-Emerick Aubameyang:
Current Rating Pierre Aubameyang88MatchPracticePassingExpectedGoalsDefensePossessionControl
798690899394949299908070605010-1111-1212-1313-1414-1515-1616-1717-18Rating of Season Performance
Aubameyang is a world-class striker
Wow, look at that development chart! After a slower start at Monaco in 10-11 (in which he was still way above average), Aubameyang soared to the level of world-class strikers like Cavani and Lewandowski in the following years. He has not only had a level of above 90 in one season, which would be quite exceptional for itself, but he also was able to maintain that level over multiple seasons. These ratings clearly show, that Aubameyang is an absolute world-class striker. His main contribution thereby derives from expected goals, in the last three seasons he reached 0.87 expected goals per 90 minutes. This kind of offensive contribution is off-the-charts! If he is going to play 40 games over the course of a season, you can assume that he will reach about 35 expected goals. These numbers could easily be converted in 35 goals, with the possibility of reaching 40-45 goals. In addition to his scoring, Aubameyang shows a very high quality in other areas of the game. His possession control is quite decent for a striker, but his level of consistency is even more impressive. He played over 4000 minutes in four of the last five seasons. In the current ongoing season, he has already played over 2000 minutes. However, because of him missing an important team meeting at Dortmund, he was suspended for two games after the Bundesliga winter break, which leads to Aubameyang having almost no match practice right now. This is the reason why his current rating is only at 88. When he will return to play more matches, his rating will rise back up to 92-95.

Giroud to Dortmund?
Dortmund is said to accept a transfer sum of 70 million Euro for Aubameyang. In addition to that sum, there were rumors that Dortmund tries to onboard Arsenal’s striker Olivier Giroud. This seems not so unlikely, since Giroud would barely see any playing time with Aubameyang joining Arsenal and Dortmund would be in desperate need of a replacement. Let us look at how far Giroud could replace Aubameyang:
Current Rating Olivier Giroud81MatchPracticePassingExpectedGoalsDefensePossessionControl
758887859089828299908070605010-1111-1212-1313-1414-1515-1616-1717-18Rating of Season Performance
Giroud is better than you might expect
Giroud started off in Montpellier, where he was a part of the title-winning team in season 11-12. After his move to Arsenal before the start of 12-13, he has proven to be capable of continuing his strong showings of 11-12 and advanced to be a world-class striker for the next four seasons. His performances peaked in 14-15 and 15-16, where he reached a rating around 90. Nevertheless, his rating decreased to 82 in the last season and he has achieved the same level so far in the current season. I can only speculate on the reason for this decline, but it was probably caused by the combination of a disappointing European Championship Final loss with France and injury trouble early in 16-17, that kept him out of finding his usual rhythm. Talking about the current season 17-18, Giroud has not seen much playing time after the signing of Alexandre Lacazette. His statistics are mainly coming from the Europa League, where he played every match over the full 90 minutes.

Comparing him to Aubameyang, Giroud’s contribution is more diversified. He reached 0.5 expected goals per 90 minutes over the last four seasons, which is actually very good, but still way less than Aubameyang’s 0.87. Adding to that, his possession control is less valuable than Aubameyang’s and he plays less minutes as well. Nevertheless, the statistics show that Giroud is more of a team player, his contribution concerning passes and defense is very high for a central striker.

Having analyzed Aubameyang and Giroud, let us see how they would actually match up against the other candidates who could play as central strikers for Dortmund and Arsenal. The competitors of Aubameyang would be Lacazette and Welbeck, competitors of Giroud would be Isak and Schürrle. When you look at the ratings, keep in mind that the ratings for both Aubameyang and Giroud are lower than they could be, because they don’t have much match practice right now. If they start to play more matches, you can easily add three points to the ratings of both players.
GiroudAubameyangLacazetteWelbeckIsakSchürrle898268828188Current Competitor Ratings
To keep it short: Both players would enhance the quality on the central striker position if they switch clubs. Even though Lacazette is world-class, Aubameyang’s statistics are slightly better, so playing with Aubameyang instead of Lacazette would not hurt Arsenal. However, having two world-class strikers in your team, it would be a waste to bench one of them. Therefore, I think Arsenal will establish a formation with two strikers. Danny Welbeck is a decent striker as well, so he could replace either Aubameyang or Lacazette if one of them is not able to play. Speaking about Dortmund, Giroud is definitely better than young striker Alexander Isak. Comparing him to Schürrle, who plays the central striker role on a unregular basis, Giroud should still be the better choice. Schürrle is not as bad as he is said to be, but he is very inconsistent as his stats shift from world-class to mediocre during the past few seasons.

Looking only at the statistics of Aubameyang, Dortmund should do anything to keep him at the club. His expected goals value per 90 minutes is off the charts for the last four seasons and is only matched by Edinson Cavani and Robert Lewandowski. Additionally, his decent possession control and his ability to play many matches during a season make him an absolute world-class player who will be tough to replace. That being said, if he does not want to continue to play for Dortmund and they do not like his current attitude towards the club, a swap with Giroud would not be a bad idea, since Dortmund would be in need of a quality-striker and Giroud would assumably like to play more matches. But even with the addition of Giroud, Dortmund would still have to compensate a loss of 0.37 expected goals per 90 minutes, which would equal an amount of 12 goals less per Bundesliga season. The people that like that the most are probably the ones sitting in the management of RB Leipzig and Bayern Munich, since it will decrease Dortmund’s overall quality. Looking at the other side, this will be a great signing for Arsenal. Their offensive lineup would consist of Özil, Mkhitaryan, Lacazette and Aubameyang which would be one of the best offensive lineups in the world and could make them a strong title contender in the next Premier League season.

Nigel de Jong

What a huge surprise in the winter transfer window! After having played 46 matches in the Champions League and 81 matches for the national team of the Netherlands in his career, 33-year old defensive midfielder Nigel de Jong has signed a 5-months contract with Mainz 05 a few days ago. With his last four stations being Manchester City, AC Milan, LA Galaxy and Galatasaray, you could not immediately conclude, that he would end up playing for Mainz 05. However, is it really a good signing for the club or has de Jong’s quality decreased as he became older? Let us look at his current Footballelixir rating:
Player Rating Nigel de Jong70ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
Still an above-average player
When you think about Nigel de Jong, you think about defensive plays: Possession gains, fouls, hustle plays and a ton of yellow cards. The graphic above confirms that, his main quality is on the defensive side of the game. Over the last three seasons he averaged impressive numbers of 1.9 successful tacklings, 3.1 interceptions and 1.2 blocks per 90 minutes. His defensive contribution goes along with a decent possession control. Although he is quite often in possession of the ball with about 65 passes per 90 minutes, he loses possession very rarely. His passing itself is solid, de Jong is however not the kind of player that provides you with a lot of key passes due to his defensive positioning on the pitch. Alongside to that comes his little contribution concerning expected goals. The biggest minus right now is his non-existent match practice, he has not played in a competitive match for over a year. If that is going to change and de Jong will achieve more playing time at Mainz, can only be answered, when we take a look at his competitors:

There are three defensive midfielders currently playing for Mainz (Gbamin, Latza and Serdar) who can be compared to de Jong, as well as Fabian Frei, who has just left the club. Let us look at their current player ratings, which are composed without match practice, to draw a comparison between them:
Nigel de JongNigel de JongFabian FreiFabian FreiJean GbaminJean GbaminDanny LatzaDanny LatzaSuat SerdarSuat Serdar6965726870Competitors (minus match practice)
De Jong is likely to play
Looking at this quintet of defensive midfielders, de Jong has the second best overall rating. He outperforms Jean-Philippe Gbamin and Suat Serdar and is slightly better than Fabian Frei, therefore he seems like a good signing to compensate the loss of the former Mainz’ player. Only Danny Latza has a higher rating than de Jong, but he is also a little more offensive minded than the other mentioned players, so it is perfectly imaginable to see both play for Mainz in the central midfield. Going a little more into detail, Nigel de Jong does not have a huge weakness compared to the other four players. He is ranked first in possession control, second in expected goals and third in defensive contribution and passing. Quite interesting is the similarity of his statistics compared to the ones of Fabian Frei. De Jong only has a small advantage in expected goals and possession control, while their defensive contribution and passing have nearly the same values.

De Jong was not always compared to average Bundesliga players, at his former stations he played together with stars like Zlatan Ibrahimovic, Wesley Sneijder, Kaká or Sergio Agüero. However, his quality is not the same as it was in his prime. Looking at his development over the last seven seasons, a decrease in quality becomes obvious, if you consider only his statistics reached in the respective season:
7778768078727499908070605010-1111-1212-1313-1414-1515-1616-17Season Rating
You might ask yourself why the chart starts in season 10-11, when de Jong has already played several seasons before in Europe’s top leagues. Well, advanced football data has not been around forever, season 10-11 was actually the first in which you could get all-around quality data. That being said, de Jong showed a very decent consistency level, with his ratings hovering around the 78-point mark from season 10-11 to 14-15. After de Jong switched from Milan to LA Galaxy in season 15-16 though, he was miles away from the consistency level of the former years around a 78-overall rating. This decline was mostly due to his defensive contribution going down by 30% concerning interceptions, tacklings and blocks. Whatever the reason for that was, he showed improved performances in the year after, at Galatasaray his defensive contribution went back to the level of his days at Milan. However, his passing, match practice and his expected goals level went slightly down compared to his stay at AC Milan, therefore his overall rating is still 4 points lower than in season 14-15.

Given the data of the seven seasons, I would conclude that Mainz would be extremely fortunate, if Nigel de Jong turns out to perform in the same way as he has for Manchester City and AC Milan. In my opinion, it is unlikely that we are going to see an overall rating of 78 in the next 5 months at Mainz. Nevertheless, we don’t have to be too pessimistic, his performances in the Major League Soccer look like a statistical outlier and his statistics at Galatasaray showed improvement. If Mainz is providing de Jong with enough playing time, we could see him at a 74 rating again.

The signing of Nigel de Jong by Mainz 05 has the potential to be a winning situation for both sides. The club is provided with a player having a better overall quality than the remaining contenders in the defensive midfield, while de Jong gains the opportunity to shine again in one of Europe’s top 5 leagues. However, we saw in the development chart that de Jong’s ratings decreased over the years and with his last competitive match over the full 90 minutes being 11 months away, it is possible, that his level of contribution has gone down even further. Looking at a non-statistical point of view though, a player who has formerly played together with superstars of international football, should be perfectly capable to provide a middle-class Bundesliga club like Mainz 05 with a bunch of quality minutes. Welcome back to the Bundesliga, Nigel de Jong!

Marek Hamsik

What a season so far by Napoli! They have won the first 8 matches in Serie A and stand on top of the table. Outstanding for Napoli is its offensive power. Mertens, Insigne and Callejon have already a combined total of 20 goals in this season. However, their play is orchestrated by Napoli’s captain Marek Hamsik and ahead of tonight’s match between Manchester City and Napoli, I have portrayed his strengths. Have a look at his detailed player rating:
Player Rating Marek Hamsik88ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
All-around talent
His rating of 88 makes him one of the best players in the world. If you look at the distribution of strengths in the graphic above, it becomes obvious, that he is an all-around player. Scoring, passing, defending, the man does it all. First of all, his expected goals are quite high for a central midfielder. He has about 0.2 per 90 minutes, meaning he will score a goal every 5 games on average. He also shows elite passing skills, 2.3 key passes per game show, how well he puts his teammates in position. With about 86 passes per game, he is the focal point in Napoli’s offense. His decision making is very good as well, in relation to his passes and shots, he loses possession quite rarely. Moreover, he is not only providing Napoli with offense, 1.6 successful tackles, 0.7 interceptions and 1.3 blocks per 90 minutes document his defensive contribution. Marek Hamsik is an all-around player. Adding to these per-90 stats, Hamsik is very reliable. In each of his last three seasons, he played more than 4000 minutes for Napoli and the Slovakian national team, which equal 45 matches per season over the full 90 minutes. His reliability is very important, not only does Napoli have an amazingly skilled player with Hamsik, he also performs on a high level in the majority of the club’s matches in a season.

Looking at his development over the last four seasons, how did he perform if you consider only his statistics reached in the respective season?
8090898699908070605013-1414-1515-1616-17Season Rating
While Hamsik is listed only at 80 in season 13-14, which was by the way the last season where he missed one and a half months of action due to an injury, he established a high rating around 90 from season 14-15 on. His most constant statistic over the four seasons is possession control, Hamsik makes good decisions with the ball and plays a pass or shoots on goal in the vast majority of situations instead of losing possession. His passing is consistent as well, while seasons 14-15 and 15-16 were slightly better than 13-14 and 16-17. Concerning expected goals his level seems to be at 0.2 XG per 90 minutes, although his outstanding season was 14-15 with 0.3 expected goals per 90 minutes. His defensive contribution increased over the four seasons, which might be a cause of him playing a little deeper from 15-16 on, than he did in 13-14 and 14-15. Adding to that, his reliability is outstanding, Hamsik played a high amount of minutes in nearly every match Napoli and Slovakia played in the last few years.

Being 30 years old, Marek Hamsik is not only the captain of Napoli, he is their most constant player over the last years. He provided Napoli over the last four seasons in Serie A, Champions League and Europa League with 32 expected goals and 364 key passes, resulting in 40 goals and 44 assists. While he makes good decisions on offense, he contributes defensively as well. While on average 2-3 players leave a football match with minor to major injuries, his reliability is outstanding, seeing him play over 4000 minutes in each of the last three seasons. To sum it up, Marek Hamsik is probably the most high-level all-around player you can get, because he delivers in nearly every area of a football match.

Golden Boy 2017

In mid-September, Italian newspaper Tuttosport released a list of 25 nominees for the golden boy award. It awards Europe’s best player under the age of 21 under consideration of their performance in the current calendar year. The list includes one goalkeeper, six defenders, three central midfielders, seven wingers and eight strikers. Concerning the distribution of nominees across on-field positions, it seems as if offense-minded players were preferably chosen. To understand the strengths and weaknesses of the nominated players, I used data of season 16-17 and the ongoing 17-18 season to compose a rating for each player, which you can see here:
Saint-MaximinCalvert-LewinEmre MorTheoEnes UnalAaron MartinJoe GomezDolbergRashfordDiawaraChiesaTielemansAugustinBorja MayoralDembéléPulisicBentancurHenrichsBergwijnMbappéG. JesusRanking of golden boy nominees626667686970737373757676777878787879808284
Interesting result, isn’t it? Gabriel Jesus on top, Kylian Mbappé only second while the new signing of Barcelona Ousmane Dembélé is ranked only at position 7. I will go in detail on specific players later, but let us look at some general aspects first. As you can see in the graphic, the majority of players has a rating above 72, while five players are listed below 70 and three players above 80. With the average of Footballelixir ratings in general being at 72.5, the graphic shows, that 15 of the listed 21 players are rated above this general average, which makes sense, as this is a selection of the best under 21 players in Europe. But why are there only 21 of the 25 nominated players in this list? Well, with Reece Oxford, Dominic Solanke and Kyle Walker-Peters three English players could not be ranked, because they played less than 300 minutes in the given period. As I am also figuring out how to compose a rating for goalkeepers, I have not included Gianluigi Donnarumma in this list. But enough said about general aspects, let us dive into detail.

The champion
If you would ask me to name the winner of 2017’s golden boy award, I would select Gabriel Jesus. The 20-year old Brazilian, who plays for Manchester City since January 2017 put up absolute monstrous statistics in this calendar year. Have a look at his detailed rating:
Player Rating Gabriel Jesus84ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
Starting with his scoring ability, Jesus’ expected goals value per 90 in this calendar year was completely outstanding. He had nearly 0.7 expected goals per game, which is an absolute elite value, that only world-class-strikers like Cristiano Ronaldo or Luis Suárez reach. Of course, we need to regard in this comparison, that Ronaldo and Suárez have reached this value over multiple seasons, whereas Jesus’ stats came only off 1000 minutes playing time in this year, as the award only looks at the performance in 2017. He still needs to prove this level over multiple seasons, but he looks absolutely promising. And it is not only about his scoring ability, he also makes good decisions with the ball. His possession control value is pretty good for a striker, because he loses the ball only 4 times per 90 minutes in relation to 32 passes and 3.5 shots. These 32 passes are actually pretty outstanding as well, as strikers tend to play much less passes. 25 of these passes find a teammate, with one of them being an assist to a shot on goal, giving him a decent passing rating as well. Manchester City’s number 33 is even slightly contributing defensively, on average he wins a tackle and blocks a pass every 90 minutes. Adding to these amazing stats, I have to outline, that his rating for this analysis was capped at 84, because his sample size of minutes in 2017 is only around 1000. Otherwise, his rating would have been at 94. Yes, you read that right, at 94 (!). But even the 84 he received positions him at the top of this ranking. Gabriel Jesus would totally deserve to win the golden boy award, but he has a powerful opponent, who is the sure favorite to win it in public perception.

The favorite
What a show Kylian Mbappé has put up in 2017. 24 goals. 11 assists. Debuted in the French national team. Will be the second most-expensive player in the history of football in the upcoming summer transfer window. Has multiplied his salary 20 times with his move to PSG. And he did all that at just 18 years of age. However, he is just at second place in this ranking, let us see why:
Player Rating Kylian Mbappé82ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
First of all, I prefer to look at the expected goals of a player rather than his actual goals, because expected goals explain the scoring ability of a player better than goals. That being said, Mbappé massively outperformed his XG-value of last season. Summarizing the 16-17 and 17-18 season, he had 14 expected goals in Ligue 1 and Champions League, yet scored 24 goals in these competitions. His expected goals value is very good though, he had 0.53 expected goals per 90 minutes, which is absolute top-class. Speaking about his passing, I unfortunately do not have data about expected assists, but his total of 43 key passes in comparison to his 13 assists in Ligue 1 and Champions League could be a signal for him overperforming in this area of the game as well. Thus, his passing is a bit lower rated than a pure look at his assists suggests. His passing rating is still very decent though. However, what Mbappé needs to improve is his possession control. He has 5.3 occasions per 90 minutes, where he is either dispossessed or loses the possession because of an unsuccessful touch. Regarding his total amount of passes and shots, that is way too much, which leads to him receiving the minimum value in possession control. His defensive contribution is little, which is okay for a striker. Concerning match practice, he put up incredible numbers for an 18-year-old. Until now, he played a combined total of 3000 minutes in 2017 for PSG, Monaco and the French national team, which is the highest value among the nominated players for the golden boy award. To draw a conclusion, Mbappé looks like the best player among the ones nominated for the golden boy award, but the story changes a bit, if you look at advanced statistics.

The hidden gem
In a ranking with Ousmane Dembélé, Christian Pulisic, Marcus Rashford and Youri Tielemans, it was surprising for me that 20-year old Steven Bergwijn playing at PSV Eindhoven is listed as the third best player, but his stats are very decent:
Player Rating Steven Bergwijn80ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
Unlike Mbappé and Gabriel Jesus, Steven Bergwijn played mostly on the left or right wing in 16-17 and 17-18, therefore his stats look a bit different than the ones displayed before. In the mentioned two seasons, he achieved 0.37 expected goals per 90 minutes, which is actually outstanding for a winger. He outclasses Dembélé, Pulisic and Rashford in that regard. Adding to his amazing XG-value, he shows decent passing skills with two short key passes per 90 and a passing accuracy of 83%. His 27 accurate passes per 90 are as well very good for an offensive-minded player. His possession control is average, meaning he does not lose the ball too often, but he can still improve in this area. However, in a ranking with a lot of players that lose possession very often, it gives him a slight advantage. His defensive contribution is a little below average concerning he is not a central striker but a winger nonetheless. Speaking about his match practice, he can still play more minutes. In 2017, he has played around 1500 minutes, while being subbed in and out in the majority of games. To sum it up, Bergwijn was absolute top-class regarding passing and scoring in the underlying time frame, while he has still room to improve in keeping the ball and contributing defensively. Once he has proven this quality in a higher number of matches, it should not take long until a top club provides a serious offer to sign him. To what he has shown so far, he deserves a very high place in the golden boy ranking.

The best defender
Although there are not many defenders included in this ranking, there is one who is listed at the fourth place, which is Benjamin Henrichs.
Player Rating Benjamin Henrichs79ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
Benjamin Henrichs is a 20-year old German defender and can play both as left or right back. He plays for Bayer 04 Leverkusen on a regular basis and has also played three matches in the German national team. What makes him so outstanding is his tremendous defending. Per 90 minutes in 16-17 and 17-18, he achieved nearly 4 successful tacklings, about 3.7 interceptions and 1.8 blocks. If you are not into defensive stats, that is a very high contribution. Naturally for a defender, his possession control is quite high. His offensive contribution is slightly below average for a wing back, he provides the offense with 0.7 key passes per 90, while his amount of expected goals is close to zero. Concerning match practice, he played 2000 minutes in 2017, which lifts his rating up a few points. Compared to the other defenders, which are nominated for the golden boy award, he outclasses each one by far in defensive actions, while his steady contribution in every other area (despite XG) helps him to reach the fourth-highest rating.

It is an illustrious class, which is nominated for this year’s golden boy award. A lot of the nominees are already playing in top clubs on a regular basis and have debuted in their respective national teams. Nearly half of them were transfered in 2017 for an aggregated transfer fee of 255 million Euro, whereas Kylian Mbappé’s transfer fee of 180 million Euro is not even included, because he will be officially bought by PSG in the upcoming summer. The ranking of the players revealed some hidden gems like Steven Bergwijn and Borja Mayoral, whereas not every player who was transferred for a high fee might be as good as he is told to be.

Franck Ribery

Tonight’s clash between Paris Saint-Germain and Bayern Munich could be one of the last Champions League matches that Bayern’s left winger Franck Ribery is playing in his home country France. Having already reached the age of 34, I wondered, how much quality Ribery still possesses. Throughout media coverage, football players are often told to get worse with age. As it is possibly true, that players at the age of 34 and later have less match fitness and cannot play every match in a busy scheduled football calendar, I am not convinced, that their match statistics become worse in general. From my experience, it is possible for some players to keep their level until they retire. Franck Ribery certainly had a very high quality level throughout his years at Bayern Munich. He has played 361 matches for Bayern in which he has contributed to 286 goals, assisting 174 and scoring 112 goals by himself. Quite impressive, but Footballelixir is of course looking at advanced football statistics. Let us outline Ribery’s quality with his player rating ahead of tonight’s game:
Player Rating Franck Ribery90ExpectedGoalsExpectedGoalsPassingPassingPossessionControlPossessionControlDefenseDefenseMatchPracticeMatchPractice
Still a 90
If you have been following the Footballelixir ratings in the past you may know, that 90 is a pretty rare and high value. It actually makes Franck Ribery one of Bundesliga’s best players. It is not surprising, that his passing skills are the biggest contributor to his rating. For an offensive-minded player he plays an incredible amount of 56 successful passes per 90 minutes. These passes include 2.6 short key passes per 90 minutes, which is top-class as well. Moreover, he does not only provide other players with chances, he has 0.23 expected goals per 90 minutes by himself, meaning, on average he will score a goal every 390 minutes. He is also very good at keeping the ball. While his total of possession losses seems average at 4 times per 90 minutes, we have to consider, that he plays about 67 passes in a full match. Considering the bunch of time he is in possession of the ball, his amount of possession losses is actually quite low, which gives him a decent rating in possession control. Personally, I was surprised about his defensive contribution. His (normalized) tacklings, interceptions and blocked passes are not as low as I thought, making him contribute in this field of the game as well. Lastly, his current match practice adds only a few points to his rating, since he has not played a lot of minutes in the past few weeks, which is by the way not untypical for Ribery, since he only played an average of 1400 minutes over the past three seasons.

Going back to the question asked at the beginning, how did Ribery develop over the last four seasons? The graphic below shows the rating he had after each season, if you only consider his statistics reached in that specific season. These ratings are calculated without match practice, since match practice is a value changing on a daily basis and does not make a lot of sense as an average over a whole season. Although the seasonal ratings might not be as precise as the general player rating due to a smaller sample size, it helps us recognize a tendency.
8990848199908070605013-1414-1515-1616-17Season Rating (without Match Pract.)
While Ribery was among Europe’s top players during the 13-14 and 14-15 season with absolute incredible statistics during that time, it seems as if he could not sustain that exceptionally high level during the past two seasons. While his statistics of season 14-15 equal a rating of 90, his ratings of the seasons 15-16 and 16-17 fell to 84 and 81. As he is not at a world-class level with these values, they are still pretty high, making him one of the best players of the German Bundesliga. The reason for his ratings decreasing is mainly scoring. While his defensive contribution, possession control and passing, remained quite steady throughout the four seasons, his amount of expected goals per 90 minutes decreased constantly in each season.

At the age of 34, Franck Ribery is still a great contributor to Bayern’s success. While he is not among the top 10 best players in the world anymore, he is still one of the best players in the German Bundesliga. His passing and possession control are incredibly good, while he is also contributing defensively. Just his amount of expected goals per 90 has decreased over the past few seasons, as we are seeing him score less often nowadays. Unfortunately, injuries have caused him to play less minutes over the years, but in the minutes he spends on the pitch, he is still providing his team with a lot of quality. Tonight, coach Ancelotti is not starting him, but if he will be subbed in, I expect him to have an impact to the game, as this is probably one of his last big matches in his home country France.

Player Ratings

Today, we are provided with a huge amount of statistics for players in top competitions. We have shots, passes, interceptions, tacklings, aerial duels, blocks and more statistics, but it is still to determine, which of these stats really matter and which of them are more important than others. Additionally, all these stats have different averages and ranges, which makes it even harder to compare players. In my opinion, it would be ideal to have one value for a player to describe his quality. This value could be displayed in the lineup before the start of a match to show spectators, how good the players of the two teams are. Certainly, there are already ratings for players out there, most of them coming in the form of grades. However, these ratings are usually not transparent at all, meaning we do not know how these ratings are composed. Moreover, a lot of them seem to be influenced by opinions more than statistics. As a consequence, I decided to create player ratings solely based on real in-game statistics. But how can you compose these ratings? At first, we have to see in which states a player can be during a match.

States of a football player in a match
The first and most easy state to analyze is a player being in possession of the ball. While he has the ball, he can move around with it, pass, shoot, dribble, clear the ball or lose the possession. Fortunately, all these actions with the ball are statistically recorded.

The second possible state is, that he is not having the ball while his team is in possession. This is certainly harder to analyze, since there is no accessible data about offensive off-the-ball positioning of a player on the pitch. Therefore, we just have to assume, that if a player has a good position on the pitch, he will receive the ball from his teammates.

The third possible state is his positioning on the pitch while the opposing team is in possession of the ball. Again, there is no accessible data on the defensive positioning of a player on the pitch, so we have to assume, that the more statistically tracked defensive actions like blocks, tackles and interceptions a player has, the better his defensive positioning is.

Calculation of the ratings
These three states provide us with enough influential factors to compose a player rating solely based on real in-game statistics. To do that, I have identified 5 key factors, which are used to calculate a rating for each player:

1. Expected goals: Expected goals is about the valuation of a shot under the consideration of important criteria, which determine to which degree a shot turns to a goal in average. If you want more information on my expected goals model, see this article for more information: Expected goals

2. Passing: To depict the passing ability of a player, I have created an aggregated passing value, which considers and weights publicly available passing statistics. I have to admit though, that this is the most improvable part of the ratings since I currently do not possess information on how many players were outplayed by a pass and how many expected assists a player had. As soon as I have data on these, I will consider it in the ratings.

3. Possession control: A metric I created to display, how often a player loses possession in relation to the passes and shots he takes. Every statistical analysis I made, that investigated influential factors on success in a football match showed, that this is a very important metric, although I have not seen it being widely used in the football analytics community.

4. Defensive positioning: As stated before, I do not have access to positioning data, but I have access to statistics about defensive actions performed by a player, which will be aggregated to calculate a value of how good a player’s defense is.

5. Match practice: Another metric I have been using for years, that describes how many matches a player has played during recent weeks. As well as possession control, I have not seen it used elsewhere, although it is highly significant in my statistical research models.

Player Rating – Example
After you have seen the 5 key factors, let us look at the rating of a specific player. Below you can see the rating of Cristiano Ronaldo right before the 16-17 Champions League Final. As Ronaldo is mostly known for his scoring, it is not surprising, that more than half of the points in his rating are coming from expected goals. He is also convincing in other categories, with his values in passing and possession control being decent for a striker. His defensive contribution is expectably low due to the fact, that he is an offensive-minded player. Regarding match practice, he is one of the best by playing many games in a season over 90 minutes. His values in the mentioned five categories sum up to an overall rating of 94, which (not surprisingly) outlines him as one of the best players in my database.
Player Rating Cristiano Ronaldo94ExpectedGoalsPassingPossessionControlDefenseMatchPractice
Characteristics of the ratings
After that glimpse at Ronaldo’s rating, how do the ratings look like in general? Each player rating is a number between 0 and 99. As the rated players are all playing on a professional level in top leagues, they will receive a minimum value of 50, while only outstanding players will reach a rating above 90. About 40% of the player ratings are between 70 and 75, with the average player rating at around 72.5. The ratings are balanced among all positions on the pitch, meaning the average rating for a striker does not differ from the average rating of a defender. You can see how the player ratings are distributed in the following graphic.
90-9960-6555-6050-5585-9080-8575-8070-7565-7040 percentDistribution of player ratings
Ratings of 16-17 CL Final
To give you an idea, of how the ratings look for several players, I have composed the ratings for Real Madrid and Juventus Turin right before the 16-17 Champions League Final which you can see in the graphic below. With Real and Juventus being two of the best teams in Europe, it is not surprising that all player ratings are above the average rating of 72.5, 3 players have even received one of the extremely rare 90+ ratings. The average player rating for Juventus stands at 82.6 while the average rating for Real is at 84.
GK73798883828287769086#JUVRMACHAMPIONS LEAGUE FINAL 16-17JUVENTUS TURINBarzagliChielliniBonucciBuffonDybalaHiguainMandzukicPjanicKhediraDani AlvesAlex Sandro
GK76847884898279829294#JUVRMACHAMPIONS LEAGUE FINAL 16-17REAL MADRIDVaraneRamosCasemiroNavasIscoBenzemaRonaldoModricKroosCarvajalMarcelo
The Toni Kroos problem
Looking at the rating of Toni Kroos in the above graphic, a challenging problem becomes obvious. Toni Kroos is a German midfielder, who has won the Champions League three times and was victorious as well in the 2014 World Cup with Germany. While many people say, he is an outstanding world-class player, it is difficult to reproduce his value with the common, publicly available statistics. Sure, his rating of 82 in the above graphic is far from being average, he is among the top 10% players in Europe’s top 5 leagues. However, the value Toni Kroos provides for a team is not about him having a high value in expected goals, assists or possession gains, the value he provides is that he outplays a high amount of defenders with his passing. These valued passes (I will write an own article about that in the near future) are a relatively new football metric, that are unfortunately not publicly available. Therefore, I have to stick with traditional advanced football statistics which lead to Toni Kroos having a lower value than expected.

The Mikel Merino problem
While I analyzed statistics per 90 minutes of the German Bundesliga 16-17 season, Mikel Merino received the highest rating in “defense” among all players in Europe’s top five leagues, alongside decent ratings in passing and possession control. So we can conclude, that he is one of Europe’s best midfielders? Well, maybe he is, but his incredible per 90 stats came from just 293 minutes of playing time in the German Bundesliga, which is a very small sample size to use for the calculation of a player’s rating. A player’s rating becomes more convincing, if you select statistics of a large sample size. To do that, I am using the data of a player’s last three seasons played (if the player has played in a league were advanced player statistics were tracked). However, I think that a small sample size of a player’s actions is better than rating him with default values, I just do not want a player to be too good, if he has not played a lot of minutes. Therefore, players with a small sample size cannot overpass a certain maximum value. This maximum value increases with a player having more minutes played.

Developing a one-value player rating was fun! The ratings make it tremendously easy to compare players and see, how much individual quality a team possesses. By calculating the ratings solely off in-game statistics, it is guaranteed, that the values are objective. Additionally, I was quite surprised that they serve as a good predictor for the outcome of a football match. Looking at the last four seasons of Premier League, LaLiga, Ligue 1 and Bundesliga, 69.5% of the matches that did not end as a draw were won by the team with the higher rating. If you increase the rating of a home team slightly to reproduce home advantage, the number gets higher with now 72% of the no-draw matches are won by the team with a higher rating. As the ratings add value, I will start to publish them before a match starts, to give interested readers an insight, on how good the players are, that are facing each other in the upcoming match. Other ideas involve detailed portraits on single players and how their overall rating and the rating in the 5 key factors developed over time.

Value Betting

Since I have recently started posting bets here and on Footballelixir’s twitter account I thought it might be interesting for you to see how I approach betting. Starting with the essentials of money management, I will then show how value betting works and how you can calculate the stakes.

Money management
When you start with betting, you should at first define your bankroll. This is the total amount of money you want to use for betting and it should be an amount of money you can afford to lose. From my experience, you should keep this bankroll for a certain amount of time and not adjust it on a weekly basis. Betting is always a cycle of winning and losing. If you raise your bankroll after a win and then lose, you lose even more money. Personally, I define my bankroll before a season starts and I keep it for the whole season. The amount of money you define as bankroll determines the amount of money used for a single bet. Generally said, the amount of money placed on a single bet should not be higher than 5% of your bankroll. I am working with a more conservative approach, meaning my maximum amount of money used for a single bet equals 2.5% of my bankroll.
Maximum bet(your max amount of money for onebet should be 2.5% of your capital)e.g. 10 unitsBetting capital(your total amount of moneyavailable for betting)e.g. 400 unitsMoney Management
Probabilities to win and draw
The next step is to select a match that you want to bet on. At first, you need to calculate probabilities for both teams to win and for the game to end as a draw. This can be tricky at first, but your predictions should get sharper the more experience you gain. A cornerstone to obey is that 25% of football matches end as a draw on average, while the probability of a draw usually does not go higher than 30% (at least in my predictions). Another cornerstone is that the probability of a win of a team playing at home is on average around 48%. For my predictions, I consider a lot of influential factors on a team’s success and aggregate them to receive a value for a team’s quality in a match. After comparing the calculated values for both teams, I am then calculating the probabilities for a win of each team and the game to end as a draw. The graph below shows, how a team’s probability to win could behave from the quality of the opposing team being far inferior to even to far superior in comparison to the respective team. Logically, if a team is superior, it is more likely to win, while a draw is the most likely, if the quality of the teams is considered even. The course of the lines in the graph is interesting however, as it is not linear. While the winning percentage increases dramatically, with only a minor gap between the quality of the teams, the increase in winning percentage gets lower, the higher the difference between both teams becomes.
100%80%60%40%20%0%Team 1 strongerTeam 2 strongerProbabilities:Team 1, Team 2,Draw
Calculating your stakes
So, you are having the probabilities now, what to do next? The next step is to compare the probabilities to the odds offered by your bookmaker. You can calculate these by dividing 1 / odd on an event. After that, you can calculate the margin between your probabilities and the ones of the bookmaker. If your probability on the outcome of a specific match is higher than the probability of the bookmaker, you can bet on it. If it is similar or lower, you should not bet on it. The positive margin between your calculated probabilites and the probabilites of the bookmaker is what you actually call “value”. By doing this, you can calculate your stakes on a bet as well. Simply put: The higher the margin is, the more money you should bet on it. From my experience, the highest possible margin is 30%, which I would see as the maximum bet. Let us look at an example:
2/102/1072%12%16%1.1615.349.82XReal MadridValenciaCalculating stakes
Last Sunday, on the second matchday of LaLiga, Real played at home against Valencia. As usual, Real were huge favorites among bookmakers with odds around 1.16, but did not manage to win at the end with the game resulting in a 2-2 draw. Looking at the above posted graph of probabilites of a game result we can see, that the probability of a draw is rarely below 10%, which equals 1 / 9.82. As an experienced bettor, you might have said that Real is not as strong as the odds suggest playing against a decent Valenica squad without their star players Ronaldo and Ramos. Let us say you rated the probability of a draw at 16% and the probability of an away win at 12%. Both predictions would mean a respective 6% difference compared to the odds. As we stated before, the maximum bet (10 units) is at 30% difference, meaning for every 3% difference I am betting a unit. The 6% difference therefore results in a bet of 2 units on both outcomes.

Contrary to bets that are based on intuition, value betting forces you to calculate your own probabilities of a match’s outcome and to compare it with the bookmaker odds. It certainly helps to establish a consistent stake/money management and it might improve your predictions as well. It is a promising and lucrative method which will be used entirely for the bets posted on Footballelixir and its twitter account.

Time Analysis: Nice vs. Napoli

After losing the first game 2-0 to Napoli, Nice were under pressure to score and win by at least a margin of 2 goals at their home ground against Italian side SSC Napoli to advance into the Champions League group phase. From the start on however, Napoli was the better side by playing dominantly in the first half, where you could not sense a great urgency by Nice to score, they could rarely advance into Napoli’s box. Despite that, the game was pretty fluent in the first half, with only 11 minutes of interruptions in it. After the break, OGC Nice came finally to life after Napoli had scored the opening goal, but they did not have many successful approaches towards Pepe Reina’s goal. In the end it was an easy win by Napoli, who could even score a second goal in the closing phase of the match. How the interruptions were spread among the teams and which amount of time was effectively played can be seen in the following:
of 3:03 minutesstoppage time02:55 minof 90 minutesregulation time64:55 minof 93:03 minutes67:50minEffective playing time
08:5813:2002:55OGC NiceSSC NapoliRefereeTotal amount of minutes spent byteams and referee
Mario Balotelli on the ground causing an injury break,after Napoli put the ball out of play.68 secondsElseid Hysaj on the ground causing an injury break,followed by Napoli's substitution Rog for Allan.74 secondsFoul by Dante on Mertens. Discussions among bothteams and the referee followed. After that, the refereepositioned the free kick right in front of the box.78 secondsLongest interruptions
12.0s14.5sDiscussion with referee31.5s38.5sInjury break28.0s30.3sSubstitution28.3s12.5sCorner kick7.0s11.5sFree kick19.2s10.8sGoal kick7.0s5.8sThrow-inAverage amount of seconds spentper interruption type
The game was not as heated as you could expect, it had few fouls and few interruptions. The total stoppage time of three minutes was justified, since the match was very fluent. An effective playing time of nearly 68 minutes is quite a lot, since the average effective playing time in football matches is around 57 minutes. This high amount of effective playing time was surely influenced by Napoli dominating the match and not offering Nice many opportunities to score. As we could expect, Napoli was not in a hurry during the match and did not rush things, although you totally have to point out, that they did not show excessive time wasting and were not faking any injuries. They took 4 and a half minutes longer than OGC Nice to continue in interrupted situations, but these 4 and a half minutes include the celebration of two goals, which takes about 45 seconds per goal in average, which makes the amount of time we waited on Napoli to continue even smaller. As a conclusion, it was a fair matchup, which did not have as much intensity as expected. Among the matches I did a time analysis on at Footballelixir, it now stands out as the match with the highest amount of effective playing time.

XG Level 1

Recently, I have depicted the value of using expected goals as an improvement of the shots statistic to gain a better understanding on the course of a match or the scoring abilities of players, which you can find here: Improving shot statistics. As described, the more criteria are used to weight a shot, the more precise a probability becomes to determine, to which degree a shot turns to a goal in average. However, the more criteria are used, the more complex the weighting of a shot becomes. A complex weighting may be interesting for prediction models and deep statistical analysis, but what if you just want to see which team has been better in a match by looking at some simple match statistics? That is what I want to do in this “Level 1 – Expected Goals” approach. I identified five different shot types and calculated the probabilities for them to turn into a goal. Important note: These five shot types exclude each other. For example, penalties are not included in the clear-cut chance calculation or a shot from the 6-yard box is not included in the calculation of shots from the 18-yard box.
Looking at the probabilities of these five different shot types, you can get a feeling on whether or not a shot was likely to be converted into a goal. With these five occasions, it should be possible to calculate your own expected goals value for a match by only knowing some additional information of the shots in a match. For example, the number of shots inside and outside the box can be found for many matches and whether there has been a penalty can be found anyways. The only more sophisticated occasion is the clear-cut chance, which cannot be found in every match statistic, but with football analysis advancing further, I expect it to be used more often.

Improving shots

The amount of shots is often used alongside possession and pass success to describe, which team played better in a match in addition to the result. However, a team can shoot the ball 5 times from 30 yards out or 5 times from 6 yards away from the goal, so the shot statistic unfortunately gives us no indication, of how dangerous the shots of a team were and how likely they were to turn into a goal. Nevertheless, it would be definitely an improvement for shots to be weighted thus spectators could see, which team had better chances in the game and therefore was more likely to win.

Expected goals
Certainly, this approach came up as soon as additional data to shots was tracked. It is usually called “expected goals (XG)” as it shows you, how many goals you can expect on average according to the shots taken. There are quite a few different models out there, where football analysts weighted shots according to different criteria. Often-used criteria for weighting the shots are the shot location, the angle to the goal, the situation of play and whether the shot was a header or not. The more criteria are used to weight the shot, the more precise a probability becomes to determine, to which degree a shot turns to a goal in average.

Where can you find XG?
Articles on different XG-models can be found on the websites of various football analysts, but they usually do not publish player-based expected-goals data on a regular basis. For matches however, you can find data on expected goals on 11tegen11’s twitter account. He tweets match-based XG-data for a ton of matches on a regular basis. Fortunately, expected goals have found their way into mainstream football coverage from this season on with BBC’s football show “match of the day” displaying the expected goals value for certain matches and players. The expected goals value used is directly provided by Opta.

XG on Footballelixir
I am running my own expected goals model, which values shots by looking at 4 factors: The shot location, whether it was a clear-cut chance or not, the situation of play and whether it was header or not. Blocked shots are left out of the equation, except if the blocked shot was a clear-cut chance. The analysis is based on a total of 500000 shots and should therefore be precise. It is less detailed than the models by Opta or 11tegen11 however, because I simply do not have as much information on shots. The biggest weakness is, that I do not have exact coordinates on the location of a shot, and can only distinguish by whether a shot was taken from the 6-yard box, the 18-yard box or from outside the box. Assists on shots are not contained in the analysis as well. Nevertheless, the model certainly has its value, since it is a better predictor for a player’s offensive capability than the amount of his shots and goals. It will be used to describe the offensive prowess of players, the performance of a team in a match and it serves as a key factor in my predictions.