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FPL Defensive Contributions 2026/27

Every Premier League outfielder ranked by how often they clear their own DefCon threshold.

Player Explorer Rate My Team Team Builder Fixture Ticker Defensive Contributions Rankings
DEF 10 actions in a match

Clearances, blocks, interceptions and tackles.

MID FWD 12 actions in a match

The same four, plus ball recoveries.

2 points, once per match

Ten actions and twenty actions pay exactly the same.

Most reliable DEF John Egan DEF HUL · £4.1m · 6.8% owned Mostly clearances · 9.3 a game
82% of his last 4 starts
Most reliable MID Exequiel Palacios MID IPS · £5.0m · 0.0% owned Mostly recoveries · 8.5 a game
88% of his last 2 starts
Most reliable FWD Charalampos Kostoulas FWD BHA · £5.5m · 0.7% owned Mostly recoveries · 4.2 a game
11% of his last 5 starts

DefCon leaderboard

80 players
Premier League outfield players ranked by the share of matches in which they clear their Fantasy Premier League defensive contribution threshold, 2026/27 season. Defenders need 10 defensive actions, midfielders and forwards need 12. Minimum 180 minutes played.
# Player Price Owned Mins Actions per 90 vs threshold Hit rate Where the actions come from Pts/90 Per £m
1 Exequiel Palacios MID IPS £5.0m 0.0% 195 17.5 88% 2 of 2 starts 53% recoveries 1.75 0.35
2 John Egan DEF HUL £4.1m 6.8% 360 13.8 82% 3 of 4 starts 57% clearances 1.64 0.40
3 Nobel Mendy DEF HUL £4.1m 7.3% 249 14.1 75% 2 of 4 starts 56% clearances 1.51 0.37
4 Tarik Muharemović DEF LEE £5.0m 4.0% 360 12.5 74% 3 of 4 starts 54% clearances 1.49 0.30
5 Shea Charles MID FUL £5.0m 0.1% 213 13.9 69% 1 of 2 starts 57% recoveries 1.37 0.27
6 Ethan Pinnock DEF COV £4.5m 0.1% 192 11.7 65% 4 of 6 starts 54% clearances 1.31 0.29
7 Elliot Anderson MID MCI £6.3m 4.2% 323 12.5 65% 28 of 38 starts 58% recoveries 1.30 0.21
8 Anan Khalaili DEF CRY £5.0m 0.3% 289 11.8 64% 2 of 3 starts 37% tackles 1.28 0.26
9 Xaver Schlager MID NFO £5.0m 0.1% 286 13.2 63% 2 of 3 starts 43% recoveries 1.26 0.25
10 Maxence Lacroix DEF CHE £6.0m 7.4% 274 11.5 61% 23 of 38 starts 53% clearances 1.21 0.20
11 Mamadou Sangaré MID BRE £5.7m 12.1% 300 13.5 59% 3 of 4 starts 47% recoveries 1.17 0.21
12 James Garner MID EVE £6.0m 0.2% 238 13.6 55% 20 of 38 starts 42% recoveries 1.10 0.18
13 António João Pereira de Albuquerque Tavares da Silva DEF BOU £5.0m 0.4% 360 10.3 55% 3 of 4 starts 56% clearances 1.10 0.22
14 Chris Richards DEF CRY £5.0m 0.6% 360 11.0 55% 18 of 35 starts 51% clearances 1.10 0.22
15 James Hill DEF BOU £5.5m 0.8% 360 8.8 53% 17 of 26 starts 51% clearances 1.06 0.19
16 Rodrigo Bentancur MID TOT £5.5m 0.2% 287 13.5 53% 14 of 26 starts 45% recoveries 1.06 0.19
17 Wesley Fofana DEF CHE £5.0m 0.2% 242 11.9 52% 9 of 23 starts 45% clearances 1.05 0.21
18 Sven Botman DEF NEW £5.0m 0.7% 360 10.3 52% 13 of 25 starts 46% clearances 1.05 0.21
19 Bobby Thomas DEF COV £4.0m 9.0% 347 10.1 52% 2 of 4 starts 54% clearances 1.03 0.26
20 James Tarkowski DEF EVE £6.1m 13.2% 360 9.8 51% 21 of 38 starts 49% clearances 1.03 0.17
21 Vitaly Janelt MID BRE £5.0m 2.4% 360 13.8 50% 9 of 19 starts 41% recoveries 1.00 0.20
22 Ethan Ampadu MID LEE £5.4m 1.0% 360 11.5 50% 21 of 38 starts 43% recoveries 1.00 0.19
23 Dara O'Shea DEF IPS £4.0m 2.9% 360 9.8 49% 2 of 4 starts 50% clearances 0.98 0.25
24 Nordi Mukiele DEF SUN £5.4m 2.0% 270 13.0 49% 15 of 35 starts 40% clearances 0.98 0.18
25 Kobbie Mainoo MID MUN £5.5m 1.5% 275 14.4 49% 9 of 19 starts 55% recoveries 0.98 0.18
26 Alex Scott MID BOU £6.1m 5.9% 356 13.2 48% 18 of 38 starts 50% recoveries 0.95 0.16
27 Nico Elvedi DEF LEE £4.5m 0.0% 236 9.5 47% 1 of 2 starts 68% clearances 0.93 0.21
28 Jaydee Canvot DEF CRY £4.9m 0.2% 270 8.0 46% 9 of 17 starts 44% clearances 0.92 0.19
29 Murillo Costa dos Santos DEF NFO £5.5m 1.9% 359 10.5 46% 13 of 29 starts 41% clearances 0.92 0.17
30 Jarrad Branthwaite DEF EVE £5.5m 2.1% 360 10.0 46% 5 of 11 starts 41% clearances 0.91 0.17
31 Joachim Andersen DEF FUL £5.0m 0.1% 180 7.5 45% 20 of 35 starts 51% clearances 0.91 0.18
32 Kristoffer Ajer DEF BRE £4.5m 4.4% 360 10.3 45% 11 of 24 starts 50% clearances 0.90 0.20
33 Daniel Ballard DEF SUN £4.9m 2.9% 360 8.8 45% 16 of 28 starts 55% clearances 0.89 0.18
34 Kevin Danso DEF SUN £5.0m 0.2% 180 6.0 44% 13 of 19 starts 55% clearances 0.89 0.18
35 Granit Xhaka MID SUN £5.5m 4.0% 360 11.5 44% 16 of 36 starts 51% recoveries 0.87 0.16
36 Tyler Adams MID BOU £5.0m 0.3% 192 13.1 43% 9 of 23 starts 44% recoveries 0.86 0.17
37 Nathan Collins DEF BRE £5.5m 1.2% 196 7.8 42% 17 of 35 starts 46% clearances 0.84 0.15
38 Nikola Milenković DEF NFO £5.5m 1.4% 270 10.3 41% 15 of 38 starts 50% clearances 0.82 0.15
39 Virgil van Dijk DEF LIV £6.5m 16.7% 360 8.8 40% 16 of 38 starts 60% clearances 0.80 0.12
40 Jan Paul van Hecke DEF TOT £4.9m 6.8% 360 8.5 38% 14 of 38 starts 43% clearances 0.77 0.16
41 Gabriel dos Santos Magalhães DEF ARS £8.0m 23.5% 360 8.0 38% 13 of 34 starts 52% clearances 0.77 0.10
42 Adrien Truffert DEF BOU £5.5m 3.2% 360 9.5 37% 14 of 38 starts 38% recoveries 0.75 0.14
43 Declan Rice MID ARS £7.4m 12.7% 321 9.5 37% 15 of 38 starts 46% recoveries 0.74 0.10
44 Malick Thiaw DEF NEW £5.0m 2.2% 360 8.0 35% 14 of 37 starts 46% clearances 0.71 0.14
45 Ronald Araujo DEF LIV £5.4m 2.8% 194 8.8 35% 1 of 2 starts 50% clearances 0.70 0.13
46 James Justin DEF LEE £4.5m 1.5% 360 8.8 35% 7 of 25 starts 35% clearances 0.69 0.15
47 Anton Stach MID LEE £6.0m 2.7% 357 12.1 34% 11 of 32 starts 43% recoveries 0.69 0.11
48 Issa Diop DEF IPS £4.0m 13.8% 360 8.8 31% 3 of 12 starts 43% clearances 0.63 0.16
49 Jair Paula da Cunha Filho DEF NFO £4.5m 0.2% 270 9.3 31% 2 of 9 starts 52% clearances 0.63 0.14
50 Luka Vušković DEF BHA £5.0m 3.2% 347 8.0 30% 1 of 4 starts 55% clearances 0.60 0.12
51 Adam Wharton MID CRY £5.5m 0.8% 346 8.8 29% 10 of 33 starts 51% recoveries 0.58 0.10
52 Lewis Miley MID NEW £5.5m 0.1% 306 10.6 29% 6 of 19 starts 46% recoveries 0.57 0.10
53 Mateus Fernandes MID TOT £5.8m 0.9% 204 6.2 28% 15 of 37 starts 50% recoveries 0.56 0.10
54 Iliman Ndiaye MID MCI £5.9m 8.0% 311 11.6 28% 8 of 35 starts 62% recoveries 0.56 0.09
55 Marc Guéhi DEF MCI £6.0m 18.5% 360 6.3 27% 11 of 38 starts 39% clearances 0.55 0.09
56 Ryan Gravenberch MID LIV £5.9m 1.2% 181 11.9 26% 7 of 36 starts 43% recoveries 0.52 0.09
57 Jacob Greaves DEF IPS £4.0m 1.2% 357 7.6 26% 1 of 4 starts 43% clearances 0.52 0.13
58 Jeremy Jacquet DEF LIV £5.0m 3.1% 325 7.8 25% 1 of 4 starts 35% recoveries 0.51 0.10
59 Levi Samuels Colwill DEF CHE £4.9m 1.0% 251 6.8 25% 2 of 5 starts 43% clearances 0.51 0.10
60 Dominik Szoboszlai MID LIV £7.0m 36.2% 360 8.8 25% 11 of 38 starts 56% recoveries 0.51 0.07
61 Vitalii Mykolenko DEF EVE £4.5m 3.0% 360 8.3 25% 9 of 37 starts 40% clearances 0.50 0.11
62 Mohamed Belloumi MID HUL £5.0m 3.1% 312 9.5 24% 1 of 4 starts 30% tackles 0.48 0.10
63 Harry Maguire DEF MUN £4.9m 10.2% 360 6.5 24% 9 of 23 starts 46% clearances 0.47 0.10
64 Lewis Hall DEF NEW £5.2m 14.4% 359 9.0 24% 8 of 28 starts 46% recoveries 0.47 0.09
65 James McAtee MID NFO £5.5m 0.1% 334 9.4 23% 0 of 6 starts 52% recoveries 0.46 0.08
66 Antonee Robinson DEF FUL £4.5m 1.4% 339 6.4 23% 5 of 21 starts 41% recoveries 0.45 0.10
67 Reinildo Mandava DEF SUN £4.5m 0.3% 360 7.3 22% 4 of 27 starts 35% recoveries 0.43 0.10
68 Mikkel Damsgaard MID BRE £5.5m 0.5% 197 9.6 21% 6 of 26 starts 59% recoveries 0.43 0.08
69 Tanaka Ao MID LEE £4.9m 0.3% 188 11.5 21% 3 of 16 starts 54% recoveries 0.42 0.09
70 Lewis Dunk DEF BHA £4.5m 1.5% 360 5.8 21% 9 of 35 starts 40% clearances 0.41 0.09
71 Roméo Lavia MID CHE £5.0m 0.2% 258 10.8 20% 0 of 8 starts 51% recoveries 0.40 0.08
72 Youri Tielemans MID MUN £5.8m 1.6% 312 8.7 20% 3 of 25 starts 48% recoveries 0.39 0.07
73 Neco Williams DEF NFO £5.0m 8.5% 360 6.8 19% 5 of 38 starts 40% recoveries 0.39 0.08
74 Olivier Boscagli DEF BHA £4.5m 0.3% 347 5.7 19% 2 of 13 starts 39% recoveries 0.38 0.09
75 Ola Aina DEF NFO £4.5m 2.6% 306 6.5 19% 2 of 22 starts 38% recoveries 0.38 0.08
76 Milan van Ewijk DEF COV £4.0m 10.1% 360 6.8 18% 1 of 4 starts 33% clearances 0.37 0.09
77 Abdukodir Khusanov DEF MCI £5.5m 1.5% 268 5.7 18% 5 of 18 starts 38% recoveries 0.36 0.07
78 Yasin Ayari MID BHA £5.4m 0.4% 243 5.2 18% 5 of 23 starts 57% recoveries 0.36 0.07
79 Marcus Tavernier MID BOU £6.1m 5.4% 350 9.3 18% 3 of 35 starts 60% recoveries 0.36 0.06
80 Harrison Armstrong MID EVE £5.0m 0.4% 356 8.9 18% 2 of 10 starts 47% recoveries 0.35 0.07

clears the threshold on average within 15% of it short of it the player's own threshold
clearances recoveries tackles interceptions blocks
Minimum 180 minutes played. Hit rate is the modelled chance of clearing the threshold in a start, using the match to match spread measured from our own data rather than assuming actions arrive at a steady tempo. That is what puts a defender on 9.5 actions and a midfielder on 11 on the same scale. Underneath it is what actually happened, and the final column is what those actions are made of, which the official data does not break down at all.

Where the actions come from

Average defensive actions per 90 across every qualifying outfielder at each club. Sides that spend the game without the ball generate the actions, so this is really a map of where to shop — and a warning, because the same clubs tend to concede the clean sheets you also want.

  • Brentford 9.1 2 over 50%
  • Leeds 8.8 2 over 50%
  • Bournemouth 8.5 2 over 50%
  • Everton 8.4 2 over 50%
  • Ipswich Town 8.2 1 over 50%
  • Crystal Palace 8.0 2 over 50%
  • Liverpool 8.0 0 over 50%
  • Nott'm Forest 7.9 1 over 50%
  • Hull City 7.8 2 over 50%
  • Coventry City 7.3 2 over 50%
  • Chelsea 7.2 2 over 50%
  • Newcastle 7.2 1 over 50%
  • Sunderland 7.1 0 over 50%
  • Spurs 7.0 1 over 50%
  • Fulham 6.7 1 over 50%
  • Man Utd 6.6 0 over 50%
  • Man City 6.6 1 over 50%
  • Arsenal 6.3 0 over 50%
  • Brighton 5.5 0 over 50%
  • Aston Villa 5.4 0 over 50%

What are defensive contributions in FPL?

Defensive contributions, shortened to DefCon by most managers, are the defensive actions Fantasy Premier League awards points for. A defender scores two points in any match where their clearances, blocks, interceptions and tackles add up to ten or more. A midfielder or forward needs twelve, and ball recoveries count toward their total as well as those four actions. Goalkeepers are not scored this way at all; they are rewarded through saves.

The critical detail is that this is a threshold, not a rate. The two points are awarded once per match, so a defender who records ten actions and a defender who records twenty score exactly the same. That single fact changes how you should shop for the statistic, and it is where most defensive contribution content goes wrong.

Why the table above is not sorted on the average

Because the points are a threshold, what you are buying is not a player's average but how often they cross the line. The hit rate column converts each player's rate into the chance of clearing their own bar in a full match, and that is what the board is ranked on by default. It matters most when comparing positions: a defender averaging 9.5 actions is a far better bet for two points than a midfielder averaging 11, because the defender needs ten and the midfielder needs twelve. Sorted on the raw average, the midfielder looks the better player. Sorted on hit rate, the truth shows up.

FPL publishes a season total and nothing finer, so every tool built on it has to work backwards from an average and guess how those actions were spread across the season. The usual guess is that they arrive at a steady tempo. They do not. We hold the matches themselves, and across last season defensive actions varied about one and a half times as much as a steady tempo allows, because they depend on how much of the game a team spends without the ball. The same player records four one week and sixteen the next.

Feeding that measured spread into the model changes the answer in both directions: a player averaging below his threshold clears it more often than the steady assumption credits, and one averaging comfortably above it misses more often. The averages themselves come from starts only, so a substitute who made one tackle in one minute of football does not appear as a 90-actions-per-90 player. Only players past 180 minutes appear here at all.

Worth saying what we did not do, because it is the obvious move and it is wrong. Having the matches, the tempting thing is to rank players on how often they actually cleared the bar. Tested properly, by fitting on the first part of each player's season and predicting the rest, that turns out to be the worst of the options, worse even than the steady-tempo assumption. A threshold throws away almost everything: a nine-action game and a two-action game both count only as a miss. The count is shown beside each player because it is a fact worth seeing, but the ranking uses the full distribution.

What the actions are made of

The last column of the table is the part FPL cannot show you at all. Two players can clear the threshold just as often as each other and be nothing alike underneath. Last season Kevin Danso cleared it in 81% of his starts, almost entirely on clearances, averaging 7.8 a game. Elliot Anderson cleared it in 70%, and did it on ball recoveries, averaging 8.2.

Those two returns behave very differently. Clearances come from defending your own box, so a clearance-heavy defender is being paid for his team being under pressure, and a run of kind fixtures quietly takes his points away with it. Recoveries follow where a midfielder is asked to stand, which is a decision his manager makes rather than something the opposition does to him, so they hold up far better against a soft run. If you are buying defensive contributions to survive a good set of fixtures, the split is what tells you which players will still be scoring them.

How to actually use this

Defensive contributions are worth roughly two points a week to a player who reliably clears the threshold, which over a season is comparable to a handful of goals. That makes them most valuable in cheap defenders and mid-priced midfielders, where two points a week is a large share of what you are paying for, and close to irrelevant in premium attackers you own for goals. The per £m column is that argument in a number: filter to £5.0m and under and sort on it to find the budget picks doing the work.

The trap is buying the statistic in isolation. A midfielder at a club that concedes possession will rack up actions precisely because his team is defending, which usually means fewer attacking returns and fewer clean sheets. That tension is visible in the club table above: the sides at the top generate the most actions and are rarely the sides you want for clean sheets. The players worth owning are the ones who clear the threshold at clubs that also keep the ball out of their own net, and the fixture ticker is where you check the second half of that.

To weigh defensive contributions against everything else a player does, the player explorer has actions per 90 as a sortable column alongside price, minutes and every attacking metric, and the team rater prices the threshold into every projection it makes.