Champions League Player Predictability
How predictable is each Champions League player? Before every match our models publish a full probability distribution for how many shots and shots on target each player will take. This board compares those distributions with what actually happened across the 2021-22 campaign, for every player who started at least 8 matches, a full league phase. The score measures how much closer our distributions get than a baseline that knows only the player's position and his expected minutes. Higher means easier to call in advance.
| # | Player | Role | Club | Starts | Score | Shots skill | On target skill | Scorer skill | Shots / match |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Reinildo | Defender | Lille | 8 | +82.7 ±2.6 | +81.2% | +84.2% | +74.5% | 0.00 |
| 2 | Mohamed Camara | Midfielder | Salzburg | 8 | +54.8 ±8.4 | +34.8% | +74.8% | +65.7% | 0.38 |
| 3 | Julian Weigl | Midfielder | Benfica | 9 | +50.8 ±17.9 | +64.0% | +37.5% | +63.7% | 0.22 |
| 4 | Koke | Midfielder | Atleti | 9 | +49.2 ±9.4 | +32.8% | +65.6% | +71.6% | 0.33 |
| 5 | José Fonte | Defender | Lille | 8 | +42.8 ±10.0 | -7.0% | +92.5% | +84.0% | 0.38 |
| 6 | Federico Valverde | Midfielder | Real Madrid | 8 | +37.8 ±9.5 | +7.7% | +67.8% | +72.6% | 1.12 |
| 7 | Raúl Albiol | Defender | Villarreal | 12 | +37.4 ±12.9 | +31.2% | +43.7% | -37.8% | 0.17 |
| 8 | Juan Foyth | Defender | Villarreal | 10 | +36.4 ±8.0 | -3.8% | +76.6% | +16.6% | 0.40 |
| 9 | Leroy Sané | Midfielder | Bayern München | 9 | +35.3 ±13.9 | +41.0% | +29.6% | +35.0% | 3.44 |
| 10 | Daniel Parejo | Midfielder | Villarreal | 12 | +35.1 ±16.9 | +35.5% | +34.8% | -1.1% | 0.42 |
| 11 | Rodri | Midfielder | Man City | 10 | +34.5 ±10.0 | +21.3% | +47.7% | +38.2% | 0.60 |
| 12 | Rafa Silva | Forward | Benfica | 8 | +33.0 ±12.0 | +28.5% | +37.4% | +7.6% | 1.00 |
| 13 | Phil Foden | Midfielder | Man City | 8 | +31.9 ±16.1 | +25.9% | +37.9% | +21.1% | 2.50 |
| 14 | Riyad Mahrez | Midfielder | Man City | 11 | +24.8 ±13.9 | +19.0% | +30.6% | +39.0% | 3.00 |
| 15 | Karim Benzema | Forward | Real Madrid | 12 | +22.8 ±7.2 | +27.2% | +18.4% | +27.0% | 3.75 |
| 16 | Arnaut Danjuma | Midfielder | Villarreal | 10 | +22.2 ±8.9 | +22.8% | +21.7% | +10.5% | 2.40 |
| 17 | Marcos Llorente | Midfielder | Atleti | 8 | +22.0 ±14.1 | +27.2% | +16.7% | +12.5% | 0.25 |
| 18 | Fabinho | Midfielder | Liverpool | 9 | +21.9 ±24.9 | +18.6% | +25.3% | -5.9% | 0.56 |
| 19 | João Cancelo | Defender | Man City | 9 | +21.4 ±11.2 | +30.3% | +12.5% | +8.3% | 1.22 |
| 20 | Kylian Mbappé | Forward | Paris | 8 | +18.1 ±11.8 | +14.8% | +21.5% | +14.9% | 3.38 |
| 21 | Lautaro Martínez | Forward | Inter | 8 | +17.7 ±10.7 | +26.1% | +9.3% | -21.7% | 3.50 |
| 22 | Étienne Capoue | Midfielder | Villarreal | 12 | +15.3 ±18.7 | +19.9% | +10.6% | +1.2% | 0.75 |
| 23 | Mohamed Salah | Forward | Liverpool | 12 | +15.2 ±10.1 | +15.6% | +14.7% | +9.8% | 3.33 |
| 24 | Rasmus Kristensen | Defender | Salzburg | 8 | +14.8 ±6.1 | +30.3% | -0.7% | +56.6% | 1.25 |
| 25 | Kevin De Bruyne | Midfielder | Man City | 9 | +14.4 ±17.2 | +24.8% | +4.0% | -1.2% | 2.56 |
| 26 | Casemiro | Midfielder | Real Madrid | 11 | +12.2 ±9.8 | +8.9% | +15.5% | +21.2% | 0.91 |
| 27 | Toni Kroos | Midfielder | Real Madrid | 11 | +9.9 ±6.7 | +9.9% | +10.0% | -2.9% | 0.91 |
| 28 | Luka Modrić | Midfielder | Real Madrid | 12 | +9.3 ±5.6 | +12.4% | +6.2% | +54.2% | 1.00 |
| 29 | Noussair Mazraoui | Defender | Ajax | 8 | +8.3 ±11.8 | +18.2% | -1.5% | -176.8% | 1.12 |
| 30 | Marcelo Brozović | Midfielder | Inter | 8 | +8.1 ±18.3 | -6.1% | +22.2% | -2.2% | 1.38 |
| 31 | Jonathan David | Forward | Lille | 8 | +7.2 ±15.8 | +4.4% | +10.0% | -6.1% | 1.50 |
| 32 | Gilberto | Defender | Benfica | 8 | +6.0 ±15.5 | +15.8% | -3.8% | -0.3% | 0.25 |
| 33 | Trent Alexander-Arnold | Defender | Liverpool | 9 | +5.4 ±5.5 | +12.1% | -1.2% | +42.4% | 1.33 |
| 34 | Ferland Mendy | Defender | Real Madrid | 10 | +5.0 ±14.8 | +18.0% | -7.9% | +9.2% | 0.20 |
| 35 | Robert Lewandowski | Forward | Bayern München | 10 | +4.8 ±34.0 | -8.8% | +18.4% | +46.0% | 3.10 |
| 36 | Pau Torres | Defender | Villarreal | 12 | +4.5 ±11.7 | +11.4% | -2.4% | -4.5% | 0.33 |
| 37 | John Stones | Defender | Man City | 8 | +3.2 ±5.5 | +5.1% | +1.2% | -583.8% | 0.75 |
| 38 | Lisandro Martínez | Defender | Ajax | 8 | +2.2 ±7.5 | +2.5% | +1.9% | +76.0% | 0.75 |
| 39 | Alejandro Grimaldo | Defender | Benfica | 10 | +1.5 ±18.7 | +6.8% | -3.8% | +35.8% | 0.30 |
| 40 | Karim Adeyemi | Forward | Salzburg | 8 | +1.3 ±7.8 | +6.7% | -4.0% | +21.5% | 2.00 |
| 41 | Nicolás Otamendi | Defender | Benfica | 9 | +1.0 ±7.7 | +4.1% | -2.1% | +77.2% | 0.44 |
| 42 | Luis Díaz | Forward | Porto | 9 | +0.8 ±10.2 | +0.6% | +1.0% | -7.9% | 2.00 |
| 43 | Sadio Mané | Forward | Liverpool | 11 | +0.6 ±7.6 | +2.1% | -0.9% | +2.3% | 1.82 |
| 44 | Andy Robertson | Defender | Liverpool | 9 | +0.0 ±9.0 | +1.8% | -1.8% | +42.5% | 0.56 |
| 45 | Milan Škriniar | Defender | Inter | 8 | -1.2 ±4.2 | +1.7% | -4.1% | -4.3% | 1.25 |
| 46 | Vinícius Júnior | Forward | Real Madrid | 13 | -2.0 ±7.4 | -1.5% | -2.6% | -20.1% | 2.08 |
| 47 | Benjamin Pavard | Defender | Bayern München | 9 | -2.5 ±30.2 | +20.9% | -26.0% | +56.0% | 1.11 |
| 48 | César Azpilicueta | Defender | Chelsea | 8 | -2.5 ±4.5 | -8.3% | +3.4% | +2.8% | 0.38 |
| 49 | Aymeric Laporte | Defender | Man City | 9 | -2.8 ±3.7 | +2.8% | -8.4% | -272.5% | 0.56 |
| 50 | Éder Militão | Defender | Real Madrid | 12 | -3.2 ±4.2 | -6.3% | -0.0% | -91.3% | 0.75 |
| 51 | Ryan Gravenberch | Midfielder | Ajax | 8 | -3.5 ±7.3 | -0.8% | -6.1% | +37.9% | 1.12 |
| 52 | Antonio Rüdiger | Defender | Chelsea | 9 | -4.5 ±4.9 | +4.2% | -13.2% | -5.2% | 1.22 |
| 53 | Dani Carvajal | Defender | Real Madrid | 11 | -5.5 ±12.4 | -3.1% | -7.9% | +73.2% | 0.45 |
| 54 | Andreas Christensen | Defender | Chelsea | 8 | -5.6 ±7.6 | -2.1% | -9.0% | -0.4% | 0.38 |
| 55 | Thiago Silva | Defender | Chelsea | 8 | -5.6 ±10.0 | -2.0% | -9.3% | +43.0% | 0.88 |
| 56 | Jan Vertonghen | Defender | Benfica | 10 | -6.0 ±5.5 | -4.0% | -7.9% | +39.4% | 0.70 |
| 57 | Andreas Ulmer | Defender | Salzburg | 8 | -7.2 ±11.7 | -8.0% | -6.5% | +0.9% | 0.62 |
| 58 | Nicolas Seiwald | Midfielder | Salzburg | 8 | -8.3 ±11.2 | +3.1% | -19.7% | +47.1% | 0.50 |
| 59 | Brenden Aaronson | Midfielder | Salzburg | 8 | -11.3 ±16.1 | -9.3% | -13.4% | -61.1% | 1.12 |
| 60 | Joshua Kimmich | Midfielder | Bayern München | 8 | -12.6 ±12.5 | -16.7% | -8.5% | -103.4% | 1.00 |
| 61 | David Alaba | Defender | Real Madrid | 11 | -12.8 ±29.3 | -13.2% | -12.4% | -3.5% | 0.45 |
| 62 | Thomas Müller | Forward | Bayern München | 9 | -12.9 ±16.8 | -21.1% | -4.8% | -2.3% | 1.44 |
| 63 | Bernardo Silva | Midfielder | Man City | 11 | -13.6 ±8.6 | -15.0% | -12.1% | -4.9% | 0.64 |
| 64 | Virgil van Dijk | Defender | Liverpool | 9 | -14.2 ±6.3 | -13.7% | -14.8% | +6.5% | 0.00 |
| 65 | Kingsley Coman | Forward | Bayern München | 8 | -15.7 ±25.4 | -15.1% | -16.3% | -1.1% | 2.50 |
| 66 | Marquinhos | Defender | Paris | 8 | -104.0 ±32.2 | -70.2% | -137.9% | -616.2% | 0.00 |
| 67 | Alisson Becker | Goalkeeper | Liverpool | 13 | saves only | - | - | +83.5% | 1.1 saves |
| 68 | Ederson | Goalkeeper | Man City | 11 | saves only | - | - | +64.7% | 1.4 saves |
| 69 | Edouard Mendy | Goalkeeper | Chelsea | 9 | saves only | - | - | +28.3% | 1.0 saves |
| 70 | Gerónimo Rulli | Goalkeeper | Villarreal | 12 | saves only | - | - | +82.8% | 3.4 saves |
| 71 | Jan Oblak | Goalkeeper | Atleti | 10 | saves only | - | - | +93.0% | 2.6 saves |
| 72 | Manuel Neuer | Goalkeeper | Bayern München | 9 | saves only | - | - | +78.5% | 1.6 saves |
| 73 | Odysseas Vlachodimos | Goalkeeper | Benfica | 10 | saves only | - | - | +92.2% | 3.8 saves |
| 74 | Philipp Köhn | Goalkeeper | Salzburg | 8 | saves only | - | - | +49.2% | 2.0 saves |
| 75 | Samir Handanovič | Goalkeeper | Inter | 8 | saves only | - | - | +87.5% | 1.5 saves |
| 76 | Thibaut Courtois | Goalkeeper | Real Madrid | 13 | saves only | - | - | +85.0% | 4.7 saves |
Starts = matches started. Skill = how much lower our CRPS is than the baseline's on the same matches, in percent, so positive means the model wins. The smaller number next to the score is one standard error. Goalkeepers take no shots, so they carry no score and are measured on saves on their own pages. Tap any column header to sort.
How the score works, and how to read it
A high score does not mean a better player. It means a player whose output our models can call match by match. The score compares two forecasters on the matches a player started: our model, which publishes a full probability distribution for his shots and shots on target once the team sheet is out, and a baseline that knows only his position and how long he is expected to be on the pitch. We measure both with CRPS, a standard score for probabilistic forecasts, and report how much better the model is, in percent. So the score really answers one question: how much does knowing WHO he is add on top of knowing WHAT he plays.
Read the score with the standard error next to it. The skill is a ratio of two small numbers, and it is much noisier for players who barely shoot: across this holdout the spread of the score is roughly three times wider for players under 0.2 shots a match than for those above 1.2. That is why the shots-per-match column sits on this board. A defender at the top of the table is usually a defender our model correctly expects to shoot almost never, which is a real win, but it is not the same measurement as a striker at the top.
The anytime-scorer skill is published alongside and deliberately kept out of the score. On a base rate of a few percent, a single season is too thin to separate players. Everything here is season level and aggregate: these pages report the accuracy of our own forecasts, not a match-by-match statistics feed.
Club title odds and the full league-phase board live on the Champions League simulations page, and how the models work on the methodology page.