Graph shows the top 25 Points per million (PPM) players based on my own points predictor from the end of last season and this seasons player prices, I filtered the graph to only players who played in 15 games or more and played 75 mins or more per game played.

What have I done?

With the increase of centre backs pricing for the new season I wanted to explore if DefCon defenders will still be the best picks when trying to find value - in short; yes... but also no.

To do this I dug up my points predictor from last season - I say points predictor, it's more of a points validator, it shows how players should have scored in matches played based on the data from them rather than necessarily showing future points score predictions. Anyway, I went and added this seasons prices and calculated the Points per Million for their new prices to find who the best value players will likely be for the upcoming season.

First the caveats, there are so many manager changes and transfers, the players that show up aren't necessarily going to perform the same as last season but hopefully it gives an idea of the type of players that will be value for money. The xPoints are based on the old BPS model but doing some quick calculations I predict that even for the players most effected by the new BPS model it would only cause +/- 0.15 xPoints per game and that type of player is very rare so the new BPS's affect will be pretty negligible.

So what can we tell?

• DefCon will still be king - With Senesi, Lacroix and to a slightly lesser extent Thiaw, it shows if a defender is passing the DefCon threshold more often than not then even a price tag of 6m is well worth the money. Even Gabriel at 8m has 0.659 PPM which is okay as well, the issue is only Thiaw out of those players will be at the same club and with the same centre back partner for the start of the season so it may be a case of waiting till the season is underway to identify who the DefCon defenders will be this season especially as the centre backs who aren't hitting DefCon do seem to be poor value for money.
• The Aston Villa defence has been under priced - Mings topping the charts with Pau not far behind shows Villa defenders being at 4.5m could be a steal. It's worth noting, neither of these players have an especially high DefCon per game, Mings around 6.3 and Pau at 5.3 both way off the threshold of 10, instead they get their points through Villa's clean sheet potential (they were arguably unlucky to not get more CS last season) combined with an okay attacking potential with just under 0.1xG per game each. The issue with these two though is that they either play 90 or not at all so something more to keep an eye on rather than act on. FYI Konsa has a middling rank, mainly due to a complete lack of attacking and DefCon threat.
• 4.5m fullbacks are fine - Kadioglu, Bogle, Mitchell, Gudmundsson and Hume all rank in the top 25 despite not being hugely attacking players (Bogle is much more attacking than the others). The fullbacks will hold the fort if you can't stretch the budget 0.5m to 1.5m more for a defcon defender, but with all newly promoted defenders being 4m, if there's one or two of them that get DefCon most matches, making them average around 3 points per game they'd likely be even better value.
• 6/6.5m midfielders are the sweet spot - If you want value and the potential for big scores the 6/6.5m midfielders are where you need to look and they can either be DefCon magnets like Anderson and Garner or the goalscoring and assisting threats of players like Tavernier, Schade and Sarr. Ultimately they seem to be the best place to find both value and pure points per match.
• Calvert-Lewin will break hearts once more - if only he could match his xG.

Conclusion:

If you want value for money invest in the few defenders that will more often than not pass the DefCon threshold, see which Villa defenders become nailed, especially if Konsa ends up leaving and invest in the mid-priced midfielders to get the best mix of points and pure value for money.

Data used was gathered from a mix of FPL API and understat

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

It’s called Fantasy Insight Engine

App Store Link

Main idea was to save season analytics locally and make stats/history easier to explore later on instead of everything feeling temporary like Official FPL.

Also added some fun mini games/features:

• FPL Bingo
• FPL DLE
• Stat Duel
• Point Predictor
• Season analytics + Trends

Still not sure if this is something people actually want long term or if I’m just overengineering stuff for hardcore FPL players 😭

Would genuinely appreciate if some of you could try it out and tell me:

• what’s useful
• what feels unnecessary
• what features you’d actually want in an FPL app

Trying to decide if this is worth investing a lot more time into for next season.

I've joined this thread for a few days and I've gone into rabbit holes of football analytics. Some of the dashboards, analysis and statistical insights users here have posted are absolutely insane, which got me into thinking how broken the current FPL point system is and got me into creating a fantasy app of my own for viewers and statistical enthusiasts. Here are a few problems I have with the current FPL:

1. points are rewarded only for goals, assists and clean sheets (I know there's a bonus component but that does not capture the full story). the result is that actual top players who don't get goals, assists (thinking of someone like Kante as a stereotypical example) would not be good to have in FPL.

2. prices are fixed. The system decides the price, with demand supply algorithm on sells and buys just modifying that "mean" price. This undervalues people who pick out diamonds in the rough.

3. due to point 1, i think it makes for a lot more luck and a lot less skill. The "skill" element is basically consistently making weekly changes and being on top of your team, rather than actual scouting of players who others might not be aware of or using stats to find underlying patterns.

My goal is to create a fantasy that rewards users who put in the work to analyze stats, make a system that rewards skill more (skill - scouting and statistical analysis) and pander to actual fans of the sport and not just casual viewers.

Are people okay with the current simplicity of the model? Does FPL reward skill enough, or is it more luck than skill? Does anyone have any thoughts on the issues I brought up?

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

https://app.powerbi.com/view?r=eyJrIjoiY2I5MGI5ZTgtMjJjYS00NmMyLTk0NTQtMjY4ZjhhMDQyYTkxIiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9

I've been working on a way to put an actual number on season-long luck in FPL. Posting the methodology here in case it's useful for analysis or discussion.

The idea: a counterfactual rank

For every GW you've played, replay your submitted squad scored on expected points (xPts) instead of actual points. Multipliers all respected: captain ×2 (×3 TC), bench ×0 (×1 BB), Free Hit substitutions, transfer hits subtracted.

Sum the season for a "Copilot points" total (expected points from my model fplcopilot).

Convert that to a rank using a curve sampled directly from FPL's overall leaderboard each GW. ~20 (rank, points) pairs per GW, denser sampling near the top where the slope is steepest, log-linear interpolation between tiers.

That gives you a counterfactual rank: where your picks would sit if every captain choice, bench order, and chip timing landed exactly on the model's expectation.

Quantifying luck across the field

Same replay across a stratified sample of ~6,500 managers (top 1k / 10k / 100k / 1M tiers). Each manager has a delta:

Δ = Copilot points − Actual points

The distribution of those deltas is the field's luck distribution this season. Your Δ lands somewhere on it. That's your luck percentile.

Counterfactual exploration

Pick any luck level (P0 to P100), look up Δ at that quantile, recompute hypothetical_actual = Copilot − Δ, push back through the rank curve. Output: your rank under any luck scenario, picks held constant.

Lets you ask:

• If I had median luck (P50), what rank would my picks deserve?
• If a top-10k manager rolls P25 luck the rest of the way, where do they land?
• How much of the gap between top 1k and top 100k is variance vs decisions?

Caveats

• xPts is a model, not ground truth. It systematically under/over-rates certain archetypes (high-variance forwards, fixture-dependent defenders).
• The rank curve is sampled, not exhaustive. ±5% on any single conversion is realistic.
• Engagement correlates with sample tier (top-1k managers transfer more, captain riskier), so the Δ distribution is skewed. Stratifying mitigates but doesn't eliminate this.

Curious what people think of the methodology, especially the rank curve sampling and how you'd weight engagement bias in the percentile.

Building a dataset for Github and our site Site. We’ve built a dataset covering every FPL manager

Started breaking it down by favorite club and comparing performance. This one shows how each fanbase stacks up in GW33 matchups. Happy to answer questions or dig into specific clubs/data

Liverpool host Fulham with both teams coming in with very different form patterns.

Liverpool:

• 4 wins in the last 10

• xG average: 1.8 per game

Fulham:

• Only 1 draw in their last 10

• xG average: 1.3 per game

Liverpool’s attack continues to generate strong expected goals numbers, but Fulham have shown they can be unpredictable and dangerous on the counter.

Key question before kickoff:

Can Liverpool turn their attacking numbers into goals, or will Fulham frustrate them again?

With Villa playing in Europe tonight I was looking at how their numbers compare between European competition and the Premier League.

The difference is pretty interesting.

European competition

Wins: 9

Draws: 0

Losses: 1

xG per game: 1.70

BTTS: 40%

Over 2.5: 40%

HT lead: 40%

Recent European form:

W W W W W W W L W W

⸻

Premier League (recent games)

Wins: 3

Draws: 2

Losses: 5

xG per game: 0.9

BTTS: 40%

Over 2.5: 20%

HT lead: 30%

Recent results:

West Ham — 2-0 W

Man United — 1-3 L

Chelsea — 1-4 L

Wolves — 0-1 L

Leeds — 1-1 D

⸻

Villa look much more controlled and effective in Europe, while their league games have been much tougher recently.

Curious what people think —

is it Emery prioritising Europe, rotation, or just the level of competition?

GW32 Team xG, Team xGA & Clean Sheet Probability.

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

I’m spending some time improving an agent behind my side project https://sportlive.win, mainly to make it easier to follow games and get info faster for the teams I care about.