
It’s the International Break and that means it is a great opportunity to take stock of what has happened this season.
This break I am updating our Fixture Difficulty Ratings (FDRs) to make sure our ticker remains accurate and I thought it made an interesting long piece for people to read.
So, read on for lots of data, tables and analysis of the top defences, attacks and attackers.
Putting This Season Into Context
Last season saw a historic surge in goals scored. The 2022/23 season held the record for goals in a 20-team Premier League with 1,084, but 2023/24 shattered it with 1,284 goals—also surpassing the 1992/93 total of 1,222, despite fewer matches.
The BBC attributed this spike to:
- Longer stoppage times.
- Record penalty conversion rates (90.3%).
- A record 281 individual goal scorers.
Sheffield United’s poor defence contributed but didn’t fully explain the increase. While more subs per game might be a factor, that change began in 2022/23.
Clean Sheets and Goals Scored Over The Last 4 Seasons
Here’s the data, including projected totals for 2024/25 (based on current averages):
I have projected the final totals for this season based on the current numbers and multiplying that by 3.4545. So, it is not going to be 100% accurate, as not each team has played each other yet.
| 20/21 | 21/22 | 22/23 | 23/24 | 24/25 (proj) | |
| Clean Sheets | 224 | 212 | 207 | 157 | 45 (156) |
| Goals Scored | 1024 | 1071 | 1084 | 1246 | 314 (1084) |
What FPL Lessons Can We Learn From This?
- Clean sheets are still scarce, reflecting a trend from last season.
- Goals have reverted to 2022/23 levels, although there is potential for a late-season spike.
While this helps us gauge the league’s defensive patterns, identifying top defences requires more granular analysis.
Who Are The Good Defences in FPL?
There are two reasons why we might want to know this:
- Targeting Fixtures – We want to own attackers and midfielders against the bad defences.
- Owning Defenders – We want to own defenders and goalkeepers from good defences.
To identify good defences we are going to look at short-term data from this season and longer-term data from this season and last. I’ll predominantly be using xGC and clean sheets as indicators of good defences.
This is because when targeting fixtures I want to target teams with high xGC and when owning defenders I want to buy players that get clean sheets. One doesn’t always equal the other and owning a defender on a team that has a low xGC is great. But what if they have a low xGC and they never get a clean sheet? I still don’t want that guy.
Short-Term Data – This Year’s Team Defensive Data
The following table shows this year’s data from the first eleven gameweeks. It also shows:
- FDR – How hard the first 11 games (1=easy, 5=hard)
- xGC proj – a crude projection of xGC for the entire season
- xGC proj v.s. last year – this year’s projection v.s. last year’s actual xGC. A minus number indicates an improvement vs. last year.
| Team | GC | xGC | Clean Sheets | FDR (first 11) | xGC proj
| xGC proj v.s. last year | |
| 1 | LIV | 6 | 9.47 | 6 | 2 | 32.71 | -13.46 |
| 2 | NFO | 10 | 11.41 | 4 | 1 | 39.42 | -14.46 |
| 3 | ARS | 12 | 12.07 | 3 | 5 | 41.70 | +13.28 |
| 4 | FUL | 13 | 12.74 | 2 | 1 | 44.01 | -20.24 |
| 5 | TOT | 13 | 13.17 | 2 | 1 | 45.50 | -19.05 |
| 6 | MCI | 13 | 13.35 | 2 | 2 | 46.12 | +10.26 |
| 7 | AVL | 17 | 13.48 | 1 | 3 | 46.57 | -14.16 |
| 8 | BOU | 15 | 14.74 | 1 | 5 | 50.92 | -7.92 |
| 9 | NEW | 11 | 14.81 | 3 | 4 | 51.16 | -10.80 |
| 10 | CHE | 13 | 15.16 | 2 | 4 | 52.37 | -6.59 |
| 11 | EVE | 17 | 16.1 | 3 | 1 | 55.62 | -0.45 |
| 12 | CRY | 15 | 16.42 | 2 | 2 | 56.72 | +3.94 |
| 13 | MUN | 12 | 17.14 | 5 | 4 | 59.21 | -10.87 |
| 14 | BHA | 15 | 18.55 | 3 | 5 | 64.08 | +7.64 |
| 15 | WHU | 19 | 18.67 | 2 | 3 | 64.50 | -7.65 |
| 16 | BRE | 22 | 19.72 | 0 | 3 | 68.12 | +11.42 |
| 17 | WOL | 27 | 20.09 | 1 | 5 | 69.40 | +0.48 |
| 18 | LEI | 21 | 20.55 | 1 | 2 | ||
| 19 | SOU | 21 | 23.02 | 1 | 3 | ||
| 20 | IPS | 22 | 24.64 | 1 | 4 |
What does the Short-Term Data Show?
Premium Defenders
Arsenal are the best defence in the league. They have had way harder fixtures than the rest of the top 5 teams and are still 3rd best on xGC.
Man City and Liverpool have had similarly difficult fixtures but Liverpool look significantly stronger on xGC, clean sheets and projected xGC.
Target/Avoid Buying The Promoted Teams
Leicester, Southampton and Ipswich defences are awful. Target them heavily and avoid buying them.
Leif Davis might be the one exception, as he has the third highest xGI per 90 of any defender in the league at 4.5. Of course, you could buy the fourth highest xGI from the 7th best defence for 4.7 with Digne… (Maatsen is the second highest xGI per 90 among defenders, but he doesn’t count because all he does is come off the bench and of course Alexander-Arnold is top).
The Rest
Forest, Fulham and Spurs have had easy fixtures which could explain their impressive xGC so far this season. With Spurs at least, this tallies with what we see on the pitch – they are very attacking and leave a lot of space in behind for attackers.
Bournemouth, Newcastle and Chelsea have all had difficult fixtures but come out looking very good which suggests their defences are strong. Villa have had a middling time of it but look relatively solid ahead of them.
Everton, Crystal Palace and Wolves are all performing pretty much at par with last season. That’s great for Wolves who have had the toughest fixtures but concerning for Everton and Palace who have had easier fixtures.
West Ham and Brentford look awful despite only having middling fixtures. That seems consistent with what we watch when we see them play. West Ham are just a mess and Brentford just want to score goals.
Long-Term Data – This Year + Last Year
Whilst the last 11 games are a useful indication of a trend, they don’t tell us the full story. So, let’s look at the last 49 games instead.
The below table shows last year and this year and is organised by xGC total.
| Team | GC 23/4 | GC 24/5 | GC Total | xGC 23/4 | xGC 24/5 | xGC Total | Clean Sheets 23/4 | Clean Sheets 24/5 | Clean Sheets | |
| 1 | ARS | 29 | 12 | 41 | 28.42 | 12.07 | 40.49 | 18 | 3 | 21 |
| 2 | MCI | 34 | 13 | 47 | 35.86 | 13.35 | 49.21 | 13 | 2 | 15 |
| 3 | LIV | 41 | 6 | 47 | 46.17 | 9.47 | 55.64 | 10 | 6 | 16 |
| 4 | NFO | 67 | 10 | 77 | 53.88 | 11.41 | 65.29 | 4 | 4 | 8 |
| 5 | CRY | 58 | 15 | 73 | 52.78 | 16.42 | 69.2 | 10 | 2 | 12 |
| 6 | EVE | 51 | 17 | 68 | 56.07 | 16.1 | 72.17 | 13 | 3 | 16 |
| 7 | BOU | 67 | 15 | 82 | 58.84 | 14.74 | 73.58 | 9 | 1 | 10 |
| 8 | CHE | 63 | 13 | 76 | 58.96 | 15.16 | 74.12 | 8 | 2 | 10 |
| 9 | AVL | 61 | 17 | 78 | 60.73 | 13.48 | 74.21 | 8 | 1 | 9 |
| 10 | BHA | 62 | 15 | 77 | 56.44 | 18.55 | 74.99 | 6 | 3 | 9 |
| 11 | BRE | 65 | 22 | 87 | 56.7 | 19.72 | 76.42 | 7 | 0 | 7 |
| 12 | NEW | 62 | 11 | 73 | 61.97 | 14.81 | 76.78 | 10 | 3 | 13 |
| 13 | FUL | 61 | 13 | 74 | 64.25 | 12.74 | 76.99 | 10 | 2 | 12 |
| 14 | TOT | 61 | 13 | 74 | 64.55 | 13.17 | 77.72 | 7 | 2 | 9 |
| 15 | MUN | 58 | 12 | 70 | 70.08 | 17.14 | 87.22 | 9 | 5 | 14 |
| 16 | WOL | 65 | 27 | 92 | 68.92 | 20.09 | 89.01 | 5 | 1 | 6 |
| 17 | WHU | 74 | 19 | 93 | 72.15 | 18.67 | 90.82 | 5 | 2 | 7 |
What Does the Long-Term Data Show?
- Arsenal, Man City and Liverpool are the best defences in the league.
- Notts Forest longer-term data correlates with their improved short-term performance.
- Over the longer-term both Everton and Crystal Palace shoot up the rankings.
- Bournemouth, Chelsea, Villa, Brighton, Brentford, Newcastle, Fulham and Spurs all look to be in the middle of the pack.
- Man United, Wolves and West Ham all look to be among the worst defences in the league.
Bucketing the Defences for FPL
With that data, a bit of analysis and a bit of judgement I think this is the ranking of defences in the league. Another way to look at it is the attacking fixture difficulty.
| FDR | Teams |
| 5 | Arsenal, Liverpool, Man City |
| 4 | Notts Forest |
| 3 | Everton, Bournemouth, Chelsea, Aston Villa, Brighton, Newcastle, Fulham |
| 2 | Spurs, Man United, Crystal Palace, Wolves |
| 1 | Ipswich, Southampton, Leicester, West Ham, Brentford |
FDR 5 – The Top Three
Arsenal, Man City and Liverpool are the best defences in the league. The long-term data shows this. Arsenal remain the best and that makes us keen on Gabriel, Saliba and the gang. Regardless, all three are significantly better than the others, and you don’t want your strikers going up against them.
I came into this piece fully expecting to say that City are my least favourite of the big three, and they remain as such. If you look solely at the long-term data you see a slightly lower xGC for City and a slightly higher clean sheets total for Liverpool and you might think that means they are about the same but I don’t think that’s right.
But, the short-term data tells a different story. In comparable fixtures Liverpool have outperformed City.
I trust the short-term trend because this year’s Liverpool are a bit more calm and measured than last year’s. The change from Klopp to Slot has been well-managed and the system is less focused on relentless physical pressing and more focused on tactical traps.
So, I am still keen to buy Liverpool defenders and less keen on City defenders.
FDR 4 – Notts Forest are in a League of Their Own
They are second in the short-term data and third in the long-term data. It’s not luck that has led to Forest having the third most clean sheets this year. They were actually unlucky and/or shit at defending set pieces last year.
The official FPL ranking for an away game at Forest is 2 and green. That’s just wrong. Both the short-term and the long-term trends suggest this is one of the best defences in the league.
Now, it is worth noting that their fixtures have been favourable so far this year so I expect they will drop off a bit. Therefore, I think it is fine to sell someone like Aina ahead of this difficult run of fixtures, especially if you are doubled up like I am with Sels in goal.
But I still believe they have made big improvements over last season.
FDR 3 – The Steady Eddies
Everton, Bournemouth, Chelsea, Aston Villa, Brighton, Newcastle and Fulham. This lot are the middle of the pack defences based on the long-term data.
They are good to rotate or back in a good run of fixtures. You might try to avoid captaining against them but not necessarily and they are fine to play against if not great. In general, both their short and long-term data suggests they are fine.
We almost had Villa above a 3 and similarly we almost had Everton below a 3 because of their short-term form. However, we think Villa tend to underperform their xGC to clean sheet ratio and Everton are the opposite.
FDR 2 – Occasional Contributors
These guys are good to target and only good to own in short bursts of great fixtures.
You might be forgiven for thinking that Spurs defence is okay. After all, they’re fifth best this season on xGC and ahead of Man City. But, the more long-term data shows that they’re the 6th worst defence in the league and the FDR shows they had an easy run.
This tallies more with what we see in real life. We see that insanely high line and fun attacking play coming back to bite them more often than not. And, because of this, I am choosing to believe this rather than the recent trend.
Man United were FDR 1 last year. They seem to have improved that, so I don’t hate buying them for a good run of fixtures… but even then it’s a stretch, and I hate myself for buying Martinez.
Palace haven’t really fallen off a cliff this year. They are actually performing very close to their rate last year. Although, their fixtures have been on the easier side, so they are a defence you want to own only in nice fixture runs.
Wolves have also performed at the same rate as last year, and that actually means they are better this year than last. Why is that? Because they have had some of the toughest games in that first 11. Still, they are among the worst defences in the league, and they don’t look good.
FDR 1 – The Exploitables
Relentlessly target these guys and never buy them.
Brentford are not a good defence. I think you can exploit this because people seem to think they are the same team they were last year (hence so many people buying Flekken on wildcard). But they actually suck.
I appreciate there is serious danger in us taking a short-term trend and making a conclusion on it. But, as with Spurs, all you have to do is watch these guys play and you can see that they are not good at defending.
And West Ham hilariously have improved on last year. They’re just still shit.
The promoted teams are the worst in short-term and they weren’t exactly exceptional defences in the championship, particularly Ipswich.
Who Are the Good Attacks in FPL?
Looking at good attacks we are looking to understand which teams have good attacks and therefore which fixtures we can feel confident in starting a defender in.
However, team data is of limited use to us when finding good midfielders and strikers in FPL – it can only tell us how many goals a team is likely to score and we want to know who is scoring those goals and get those players on our team. So, we will also be looking at individual attacking data.
Attacking Team Stats
| Team | G 23/4 | G 24/5 | G Total | G Proj | xG 23/4 | xG 24/5 | xG Total | xG Proj | A FDR (first 11) | |
| 1 | LIV | 86 | 21 | 107 | 73 | 89.37 | 20.45 | 109.82 | 70.64 | 2 |
| 2 | MCI | 96 | 22 | 118 | 76 | 81.94 | 22.29 | 104.23 | 77.00 | 1 |
| 3 | CHE | 77 | 21 | 98 | 73 | 75.27 | 19.89 | 95.16 | 68.71 | 5 |
| 4 | ARS | 91 | 18 | 109 | 62 | 77.42 | 17.3 | 94.72 | 59.76 | 4 |
| 5 | NEW | 85 | 13 | 98 | 45 | 77.08 | 16.52 | 93.6 | 57.07 | 4 |
| 6 | TOT | 74 | 23 | 97 | 79 | 68.95 | 21.69 | 90.64 | 74.93 | 2 |
| 7 | AVL | 76 | 17 | 93 | 59 | 64.72 | 16.76 | 81.48 | 57.90 | 4 |
| 8 | BRE | 56 | 22 | 78 | 76 | 59.84 | 18.33 | 78.17 | 63.32 | 3 |
| 9 | BOU | 54 | 15 | 69 | 52 | 56.69 | 19.02 | 75.71 | 65.70 | 5 |
| 10 | BHA | 55 | 19 | 74 | 66 | 58.02 | 16.54 | 74.56 | 57.14 | 5 |
| 11 | MUN | 57 | 12 | 69 | 41 | 56.93 | 17.52 | 74.45 | 60.52 | 2 |
| 12 | FUL | 55 | 16 | 71 | 55 | 51.56 | 17.15 | 68.71 | 59.24 | 1 |
| 13 | WHU | 60 | 13 | 73 | 45 | 52.92 | 15.7 | 68.62 | 54.24 | 3 |
| 14 | EVE | 40 | 10 | 50 | 35 | 54.9 | 12.2 | 67.1 | 42.14 | 1 |
| 15 | NFO | 49 | 15 | 64 | 52 | 50.39 | 14.63 | 65.02 | 50.54 | 1 |
| 16 | CRY | 57 | 8 | 65 | 28 | 49.15 | 13.99 | 63.14 | 48.33 | 2 |
| 17 | WOL | 50 | 16 | 66 | 55 | 47.58 | 11.73 | 59.31 | 40.52 | 5 |
Quick Analysis
The long-term xG splits the teams into five relatively neat buckets:
- Both in the long and the short term, Liverpool and Man City are in the top three attacking teams. They’re absolutely elite.
- Chelsea, Arsenal, Newcastle and Spurs are a step below in the long-term. However, as you can see from Spurs short term results, if you give them a nice run of fixtures they can all compete with the big two. That being said, they are all delivering over 1.8xG per game over 49 games so you are very unlikely to get a clean sheet against them.
- The next grouping of teams is those delivering between 74xG and 82xG over 49 games: Aston Villa, Brentford, Bournemouth, Brighton and Man United.
- And the next grouping is those delivering between 59 and 69xG over 49 games: Fulham, West Ham, Everton, Notts Forest, Crystal Palace and Wolves.
- The promoted teams are the worst attacks for the season so far. They weren’t great in the championship and they still aren’t.
Bucketing the Attacks for FPL
So, here is the defensive fixture difficulty.
| FDR | xG from 49 games | Teams |
| 5 | Over 104 | Liverpool, Man City |
| 4 | 90-96 | Chelsea, Arsenal, Newcastle, Spurs |
| 3 | 74-82 | Aston Villa, Brentford, Bournemouth, Brighton, Man United |
| 2 | 59-69 | Fulham, West Ham, Notts Forest, Everton, Crystal Palace, Wolves |
| 1 | N/A | Ipswich, Southampton, Leicester |
In general, I would say there are far more great attacks (and therefore difficult defensive fixtures) than there are great defences (and difficult attacking fixtures). A clean sheet against an FDR 4 is super unlikely and against an FDR almost impossible. You really want to be playing just 2s and 1s.
It is also true that I would happily own and start an attacker from the teams rated 2 above. Raul, Wood and Cunha have all proved that they can offer value.
And that’s why we should look at…
Individual Attacking Data
I said individuals matter more, so let’s look at them.
Below is xGI per 90 data from this season, based on players who have played at least 7 of the 11 games, and have been getting consistent starts (so Nkunku, Duran etc. don’t make the list). I have switched to per 90s because it allows us to judge those who have had fitness problems a little easier.
From this season, these are the top attacking assets up to Alexander-Arnold who I think is a neat cut off point.
| Name | Team | Cost | FPL points | xA | xG | G+A | xGI | xGI difference v.s actual | |
| 1 | Haaland | MCI | 15.2 | 82 | 0.04 | 1.01 | 1.09 | 1.05 | 0.04 |
| 2 | Salah | LIV | 12.9 | 107 | 0.19 | 0.61 | 1.31 | 0.81 | 0.5 |
| 3 | Maddison | TOT | 7.6 | 52 | 0.47 | 0.31 | 0.77 | 0.78 | 0 |
| 4 | Sávio | MCI | 6.5 | 29 | 0.47 | 0.29 | 0.34 | 0.76 | -0.41 |
| 5 | Wissa | BRE | 6.1 | 54 | 0.05 | 0.71 | 1.27 | 0.76 | 0.51 |
| 6 | Palmer | CHE | 10.9 | 84 | 0.23 | 0.49 | 1.13 | 0.72 | 0.42 |
| 7 | Jackson | CHE | 7.9 | 60 | 0.04 | 0.65 | 0.94 | 0.69 | 0.25 |
| 8 | Solanke | TOT | 7.7 | 43 | 0.07 | 0.62 | 0.56 | 0.69 | -0.12 |
| 9 | Foden | MCI | 9.2 | 16 | 0.51 | 0.16 | 0.19 | 0.68 | -0.49 |
| 10 | Barnes | NEW | 6.3 | 49 | 0.26 | 0.4 | 0.86 | 0.65 | 0.2 |
| 11 | Raúl | FUL | 5.8 | 48 | 0.18 | 0.48 | 0.8 | 0.65 | 0.15 |
| 12 | Saka | ARS | 10.1 | 67 | 0.38 | 0.27 | 1.11 | 0.65 | 0.45 |
| 13 | Gordon | NEW | 7.2 | 39 | 0.24 | 0.38 | 0.32 | 0.62 | -0.3 |
| 14 | Johnson | TOT | 6.8 | 49 | 0.07 | 0.55 | 0.42 | 0.62 | -0.2 |
| 15 | Watkins | AVL | 9.0 | 55 | 0.06 | 0.56 | 0.75 | 0.62 | 0.14 |
| 16 | Isak | NEW | 8.4 | 47 | 0.1 | 0.51 | 0.71 | 0.61 | 0.09 |
| 17 | Mbeumo | BRE | 7.9 | 76 | 0.25 | 0.36 | 0.82 | 0.6 | 0.22 |
| 18 | Adama | FUL | 5.0 | 38 | 0.25 | 0.34 | 0.49 | 0.59 | -0.1 |
| 19 | B. Fernandes | MUN | 8.4 | 50 | 0.19 | 0.39 | 0.48 | 0.58 | -0.09 |
| 20 | Evanilson | BOU | 5.9 | 45 | 0.08 | 0.5 | 0.51 | 0.58 | -0.08 |
| 21 | Diogo Jota | LIV | 7.2 | 35 | 0.06 | 0.5 | 0.74 | 0.57 | 0.17 |
| 22 | Díaz | LIV | 7.7 | 64 | 0.17 | 0.38 | 0.96 | 0.55 | 0.4 |
| 23 | Son | TOT | 9.9 | 47 | 0.3 | 0.26 | 0.82 | 0.55 | 0.26 |
| 24 | Archer | SOU | 5.0 | 27 | 0.02 | 0.52 | 0.3 | 0.54 | -0.24 |
| 25 | Welbeck | BHA | 5.9 | 64 | 0.03 | 0.5 | 0.78 | 0.53 | 0.25 |
| 26 | Havertz | ARS | 8.0 | 46 | 0.1 | 0.42 | 0.45 | 0.52 | -0.07 |
| 27 | Andreas | FUL | 5.2 | 31 | 0.25 | 0.27 | 0.11 | 0.52 | -0.41 |
| 28 | Martinelli | ARS | 6.9 | 47 | 0.24 | 0.27 | 0.65 | 0.51 | 0.14 |
| 29 | O. Dango | BOU | 5.0 | 22 | 0.19 | 0.32 | 0.42 | 0.51 | -0.09 |
| 30 | Semenyo | BOU | 5.7 | 46 | 0.16 | 0.36 | 0.47 | 0.51 | -0.05 |
| 31 | Kudus | WHU | 6.2 | 23 | 0.12 | 0.39 | 0.27 | 0.5 | -0.23 |
| 32 | Eze | CRY | 6.6 | 31 | 0.16 | 0.33 | 0.22 | 0.49 | -0.27 |
| 33 | Garnacho | MUN | 6.4 | 40 | 0.06 | 0.43 | 0.49 | 0.49 | 0 |
| 34 | Madueke | CHE | 6.4 | 49 | 0.14 | 0.34 | 0.48 | 0.48 | 0 |
| 35 | Wood | NFO | 6.6 | 68 | 0.02 | 0.46 | 0.81 | 0.48 | 0.34 |
| 36 | Cunha | WOL | 6.8 | 59 | 0.23 | 0.24 | 0.71 | 0.47 | 0.24 |
| 37 | Buonanotte | LEI | 5.0 | 38 | 0.16 | 0.29 | 0.61 | 0.45 | 0.16 |
| 38 | Mitoma | BHA | 6.4 | 38 | 0.2 | 0.26 | 0.3 | 0.45 | -0.16 |
| 39 | Tavernier | BOU | 5.5 | 33 | 0.16 | 0.28 | 0.33 | 0.45 | -0.12 |
| 40 | Vardy | LEI | 5.7 | 43 | 0.07 | 0.38 | 0.54 | 0.44 | 0.09 |
| 41 | Bowen | WHU | 7.5 | 50 | 0.24 | 0.19 | 0.47 | 0.43 | 0.04 |
| 42 | Calvert-Lewin | EVE | 5.8 | 35 | 0.05 | 0.37 | 0.3 | 0.43 | -0.13 |
| 43 | Kluivert | BOU | 5.3 | 35 | 0.13 | 0.29 | 0.57 | 0.43 | 0.14 |
| 44 | Minteh | BHA | 5.2 | 21 | 0.26 | 0.17 | 0.44 | 0.43 | 0 |
| 45 | Georginho (Rutter) | BHA | 5.6 | 40 | 0.14 | 0.29 | 0.58 | 0.43 | 0.16 |
| 46 | I. Sarr | CRY | 5.7 | 18 | 0.23 | 0.2 | 0 | 0.43 | -0.43 |
| 47 | McNeil | EVE | 5.7 | 51 | 0.35 | 0.07 | 0.61 | 0.42 | 0.19 |
| 48 | Mateta | CRY | 7.3 | 35 | 0.04 | 0.36 | 0.35 | 0.41 | -0.05 |
| 49 | Rogers | AVL | 5.4 | 45 | 0.15 | 0.26 | 0.5 | 0.41 | 0.09 |
| 50 | Antonio | WHU | 5.4 | 37 | 0.12 | 0.26 | 0.27 | 0.38 | -0.11 |
| 51 | Iwobi | FUL | 5.6 | 47 | 0.28 | 0.11 | 0.38 | 0.38 | 0 |
| 52 | Smith-Rowe | FUL | 5.7 | 54 | 0.13 | 0.25 | 0.57 | 0.38 | 0.19 |
| 53 | Soucek | WHU | 4.9 | 32 | 0.09 | 0.29 | 0.27 | 0.38 | -0.11 |
| 54 | Gundogan | MCI | 6.5 | 21 | 0.17 | 0.18 | 0.14 | 0.36 | -0.21 |
| 55 | Trossard | ARS | 6.9 | 31 | 0.06 | 0.3 | 0.3 | 0.36 | -0.06 |
| 56 | Alexander- Arnold | LIV | 7.0 | 47 | 0.31 | 0.04 | 0.1 | 0.35 | -0.25 |
Premium Watch
Big surprise – Haaland and Salah are top of the table. In fact, Haaland is by far and away the best pick on an xGI basis, but in FPL points he is behind both Salah and Palmer.
There are a couple of reasons for this. As you can see, Salah, Palmer and Saka are significantly overperforming their xGI per 90, whereas Haaland is just meeting his. Furthermore, the midfielders obviously get more points for a goal and a point for clean sheet. And, Salah at least, is doing very well this year on bonus points.
We have never said Haaland is a bad pick (and actually encouraged people to hold him last week). This data shows why. He is unbelievably good.
If you want to take a risk without him (like one of us), that is absolutely fine, but don’t get on social media and start slagging others off for having the best forward in the game.
Mid-Priced Midfielders
Many of us have lamented the lack of mid priced midfielders (from 8.4 to 6.4), so I am surprised to see quite a few on this list. I think the issue is that many of them are not nailed, so only really B. Fernandes and Mbeumo have appealed so far.
I could see a world where Gordon comes back into our thinking if he can regain consistent fitness.
The pick we all missed was Maddison. Only 6.7% owned at the time of writing, the 7th highest scoring midfielder and good value for it, looking at the underlyings. Spurs have dreadful fixtures for a long time now, but we should remember this as their run in at the end of the season is fine.
Cheap Midfielders
By contrast, many of us have been delighted with the plethora of cheap midfield options (below 6.4) that are out there. I think this data can give us reassurance that they can continue to offer value.
Longer-Term Attacking Data
I wanted to provide a bit of context to the above which is only based on 11 gameweeks. The table below takes the above players and looks at their xGI per 90 over the last 49 gameweeks instead. Where that player didn’t play last year or had a tiny number of mins.
At its most simplistic, this should remove some of the “noise” in the data. In other words, it should show whether this short-term form is repeatable and that is something useful for us to look at going forward.
| LT Rank | ST Rank | Name | Team | xA | xG | xGI |
| 1 | 1 | Haaland | MCI | 0.07 | 1.03 | 1.10 |
| 2 | 2 | Salah | LIV | 0.24 | 0.72 | 0.96 |
| 3 | 6 | Palmer | CHE | 0.28 | 0.59 | 0.88 |
| 4 | 16 | Isak | NEW | 0.11 | 0.76 | 0.87 |
| 5 | 12 | Saka | ARS | 0.35 | 0.43 | 0.78 |
| 6 | 7 | Jackson (Nicolas) | CHE | 0.08 | 0.61 | 0.69 |
| 7 | 17 | Mbeumo | BRE | 0.26 | 0.43 | 0.69 |
| 8 | 23 | Son | TOT | 0.27 | 0.35 | 0.61 |
| 9 | 8 | Solanke | TOT | 0.05 | 0.55 | 0.60 |
| 10 | 15 | Watkins | AVL | 0.11 | 0.49 | 0.60 |
| 11 | 3 | Maddison | TOT | 0.31 | 0.28 | 0.59 |
| 12 | 10 | Barnes | NEW | 0.21 | 0.38 | 0.59 |
| 13 | 14 | Johnson | TOT | 0.15 | 0.44 | 0.59 |
| 14 | 35 | Wood | NFO | 0.04 | 0.55 | 0.59 |
| 15 | 19 | B. Fernandes | MUN | 0.28 | 0.31 | 0.59 |
| 16 | 32 | Eze | CRY | 0.22 | 0.36 | 0.58 |
| 17 | 9 | Foden | MCI | 0.29 | 0.28 | 0.57 |
| 18 | 42 | Calvert-Lewin | EVE | 0.07 | 0.49 | 0.57 |
| 19 | 26 | Havertz | ARS | 0.14 | 0.42 | 0.56 |
| 20 | 22 | Díaz | LIV | 0.16 | 0.40 | 0.56 |
| 21 | 21 | Diogo Jota | LIV | 0.11 | 0.43 | 0.54 |
| 22 | 28 | Martinelli | ARS | 0.23 | 0.29 | 0.53 |
| 23 | 55 | Trossard | ARS | 0.14 | 0.39 | 0.52 |
| 24 | 13 | Gordon | NEW | 0.19 | 0.33 | 0.52 |
| 25 | 18 | Adama | FUL | 0.19 | 0.30 | 0.49 |
| 26 | 52 | Smith-Rowe | FUL | 0.10 | 0.39 | 0.49 |
| 27 | 41 | Bowen | WHU | 0.16 | 0.31 | 0.48 |
| 28 | 36 | Cunha | WOL | 0.14 | 0.33 | 0.46 |
| 29 | 48 | Mateta | CRY | 0.05 | 0.41 | 0.45 |
| 30 | 11 | Raúl | FUL | 0.09 | 0.36 | 0.44 |
| 31 | 38 | Mitoma | BHA | 0.21 | 0.22 | 0.43 |
| 32 | 33 | Garnacho | MUN | 0.09 | 0.32 | 0.41 |
| 33 | 25 | Welbeck | BHA | 0.06 | 0.33 | 0.39 |
| 34 | 51 | Iwobi | FUL | 0.19 | 0.20 | 0.39 |
| 35 | 27 | Andreas | FUL | 0.20 | 0.18 | 0.39 |
| 36 | 34 | Madueke | CHE | 0.16 | 0.23 | 0.39 |
| 37 | 43 | Kluivert | BOU | 0.09 | 0.29 | 0.38 |
| 38 | 47 | McNeil | EVE | 0.27 | 0.10 | 0.37 |
| 39 | 50 | Antonio | WHU | 0.08 | 0.29 | 0.37 |
| 40 | 39 | Tavernier | BOU | 0.18 | 0.19 | 0.36 |
| 41 | 56 | Alexander-Arnold | LIV | 0.26 | 0.09 | 0.36 |
| 42 | 31 | Kudus | WHU | 0.13 | 0.23 | 0.35 |
| 43 | 30 | Semenyo | BOU | 0.11 | 0.24 | 0.35 |
| 44 | 53 | Soucek | WHU | 0.05 | 0.23 | 0.28 |
| 45 | 49 | Rogers | AVL | 0.07 | 0.21 | 0.28 |
Haaland, Salah, Palmer and Saka at the top was to be expected. However, the interesting thing is the presence of Isak ahead of Saka plus Jackson and Mbeumo just behind him. And, they’re all ahead of Son. In theory you can afford five of the top six attackers in your FPL team going forward and if you’re on wildcard I would be doing exactly that.
We will look at this table in a bit more depth in our upcoming early wildcard thoughts piece.
That’s It.
In this piece we have analysed defensive and attacking data for this season and combined it with last season to get a better understanding of where the good fixtures are. This is important because fixtures breed form and targeting fixtures is the most sure fire way to guarantee rank rises.
We will be back on Monday with an early wildcard thoughts piece.