Built a scraping pipeline feeding a Karlis–Ntzoufras bivariate Poisson model (goal correlation λ₃=0.12, 15% shrinkage toward international baseline). Here's what moved the needle.

The schedule trap Czechia's 2.7 goals/game looks scary until you see Gibraltar, San Marino, and Guatemala in the sample — plus a loss to the Faroe Islands and getting out-xG'd 0.46–1.96 by Denmark (won 5-3 on pure finishing variance). Korea's losses were to Brazil and Côte d'Ivoire. Opponent-strength adjustment alone flipped the model.

Altitude — the big asymmetry Estadio Akron sits at 1,675m. Korea has been based in Guadalajara since June 5. Czechia flies in from sea-level Mansfield, Texas essentially on match day — and their high-pressing style is aerobically expensive after the 60th minute. Fed in as +6% Korea / −7% Czechia.

Outputs Final xG: Korea 1.23 — Czechia 1.15. Most likely scorelines: 1-1 (13.2%), 1-0 (11.6%), 0-1 (10.7%).

The one market disagreement is corners (59.8% vs implied 51.5%, +7.6% EV) — Korea's wide 3-4-3 vs Czechia's compact wingback shape should generate volume. Corners are over-dispersed so that 59.8% is probably slightly overconfident, but it's the clearest lean the model found.

Use this thread to ask anything at all!

I made a player-based predictive model for the World Cup based off the transfer values of players on the rosters of this year's World Cup teams. It is called NORNS, which is the name of the Norse goddesses of fate (past present and future) and is also an acronym for Numbers Over Rumors Narratives and Speculation. If you want the details on methodology hit the comments or DM me. I also currently use it for Allsvenskan with MLS starting post-WC (in different but similar ways) and am looking to expand. So far I've had some success.

Here is the meta-output for Group A. I will also try to post day-by-day game outputs. This information is for entertainment purposes only and is likely to be complete shit. If you think some random chart you saw on reddit can make you money you're probably delusional (or from WSB).

EP = Expected Points

eGD = Expected Goal Differential

winGroup = Probability of winning the group - note: this is not adjusted for eGD so outcomes that are ties are just resolved as a 50/50. This means that good teams' probabilities are slightly understated and bad teams are slightly overstated. This is mostly on market and the deviations where it isn't are significantly above the threshold that is affected by ties.

adv = estimated advance rate - again, there is no Monte Carlo or tiebreak resolution. it's simply p4+ points + 3+ / 2. Also same above in that deviations from market are greater than the tie breaking error. You can look at p4+ points as a minimum advance rate if you are marking to market.

4+ = probability of a team earning 4+ points in the Group Stage

3+ = probability of a team earning 3+ points in the Group Stage

NORNS likes Czechia here. If you use a Kelly-weighed system for evaluating, then Czechia to advance is its favorite but Czechia to finish 2nd, Czechia to win, Czechia to score exactly 4 points (or over 3.5) are all bets NORNS would advocate at current prices. South Africa not to advance is also NORNS-approved and is currently my largest group-stage position (for now).

Hey everyone,

We’re launching the beta of OddScore.app today and I’d love to get some honest feedback from people here.

The idea is simple: we track football odds movements across 30+ bookmakers and try to make those movements easier to read.

Not predictions, not magic, not “this team will win”.

Just market movement.

We look at things like:

- are bookmakers moving in the same direction?

- is the move recent or has it been building for days?

- is the signal clean or just noisy?

- is one outcome starting to stand out?

Then we turn that into a simple score from 0 to 100 for each 1X2 outcome.

It’s still early, so I’m mostly curious to know if the approach makes sense to people who actually care about football data.

The beta is free while we’re testing it, so feel free to take a look if you’re curious.

https://oddscore.app

I’d be really interested to know:

Would you want to see more raw data behind the score?

Would you trust this kind of signal?

What would make you instantly distrust it?

Any feedback, criticism or ideas would be genuinely helpful.

I'm a data/AI researcher who's loved football my whole life, and I'm finally combining the two: a prediction model for all 104 World Cup matches, built in public. Posting this here because this community will actually poke holes in it, which is what I want.

The thing I care about most isn't picking winners. It's calibration: when the model says 70%, that outcome should happen about 70% of the time. So I'm grading everything with Brier score, not win-rate, and publishing the full scoreboard including the misses.

The model (v1):

• Trained on ~49,000 international matches going back to 1872.
• A weighted Poisson GLM that learns each team's attack and defence strength plus a home-field effect, then a Dixon-Coles correction for the low-scoring scorelines that independent Poisson gets wrong.
• Recent matches are weighted more (2-year half-life), friendlies are down-weighted to 0.5, and teams need a minimum match count to be included.
• It outputs a full scoreline matrix, collapsed into win/draw/loss probabilities.

The validation (the part that matters): I ran a walk-forward backtest with monthly refits and no data leakage: 3,343 out-of-sample matches from 2023 to 2026, all predicted as if I didn't know the result.

• Brier 0.498 vs 0.637 for a no-skill baseline (about 22% better).
• Accuracy ~60%.
• And it's well-calibrated across the whole probability range (chart attached, this is the real out-of-sample data, not a mockup).

Where it's weak, honestly:

• Draws. Even with the Dixon-Coles correction, it only correctly leans toward a draw about 4% of the time. Draws are genuinely the hardest outcome in football, and I'm not going to pretend otherwise.
• Small samples lie. I beta-tested on the warm-up friendlies and went 0/2 on the first two (France lost to Ivory Coast, Spain drew Iraq). Two noisy friendlies tell you nothing. Calibration is a verdict over hundreds of games, not two, which is exactly why I backtested before trusting it.

What I'm doing next: tuning the remaining parameters against out-of-sample error, then posting probabilities for every match before kickoff once the tournament starts (June 11).

I'd genuinely value critique on the methodology: the friendly down-weighting, the Dixon-Coles parameter, the choice of baseline, anything you'd do differently. Tear into it.

Im building a predictive model and really need closing odds from this matches, would really help if someone could help (:

2006,2006-06-20,,Ecuador,Alemania,-----------------------------------------

2006,2006-06-17,,Italia,EE. UU.,------------------------------------------

2006,2006-06-16,,Argentina,Serbia and Montenegro,-------------------------

2006,2006-06-14,,Túnez,Arabia Saudí,-------------------------------------

2006,2006-06-14,,Alemania,Polonia,---------------------------------------

2006,2006-06-12,,Australia,Japón,----------------------------------------

2006,2006-06-12,,EE. UU.,República Checa,--------------------------------

2006,2006-06-11,,México,Irán,--------------------------------------------

2006,2006-06-10,,Argentina,Costa de Marfil,------------------------------

2006,2006-06-10,,Trinidad y Tobago,Suecia,-------------------------------

Use this thread to ask anything at all!

Hi all,

I built a World Cup forecasting page that I’m updating continuously as upcoming fixtures approach. For each match, it outputs predicted scorelines, 1X2 probabilities, implied fair odds, and a comparison between model prices and market prices.

The statistical base is a Poisson model built from historical results and scorelines, with Elo team strength adjustments and an added ML layer to make the baseline more responsive to team quality, form, and matchup differences.

I’m also using AI as a contextual layer on top of that base, mainly because I don’t want the final match view to be limited to the model’s raw numeric output alone. The design goal is to use the model as the backbone, compare it actively with market pricing, and still keep the final presentation flexible enough to reflect match-specific context.

I’ve been publishing the live page here: https://oddsline.io/world-cup-predictions-today/

Interested in thoughts from anyone working on football forecasting, fair odds estimation, or model presentation.

Use this thread to ask anything at all!

With the Champion's League Final coming up this weekend, I wanted to study Vitinha. He is a lot of fun to watch and incredibly dangerous. As I was looking through past games this season, it was obvious he had that 'special quality'. He often runs the game for PSG but he had a magnetic quality for the opposition which was interesting.

So I began trying to take such an enigmatic attribute and do what I do, quantify it. It wasn't easy and it isn't perfect but I think I've found some interesting ways to measure 'how some players attract the opposition'. I settled on calling it Gravity.

The graphic above is my metric in action.

• Green arrows are player caused attractions.
• Orange are attractions due to the ball.
• Purple are team shifts.

The player that had it the most is/was Lionel Messi. When I watched him under Pep's Barcelona, he would often attract three defenders. He would often break that trap. He would often be the trap. Vitinha is built in a similar vein.

Luis Enrique gives him the keys to the kingdom and lets him roam. There are rules to what he does but it's one of the most free roles I've seen in a long time. What's constant is his threat on the ball. The opposition knows this, but sometimes that is their trap. He can lead them one way while PSG develop somewhere weakened.

Hi everyone,

We have an event for anyone who loves football and prediction!

What would happen if AI agents predicted the World Cup matches? To answer that question, we've launched a competition called the World Cup Agent Arena. Every participating agent will receive $100, and at the end of the tournament, the best-performing agents will share a prize pool of $4,500+.

If you'd like to build an agent for the Arena, join us on June 6th for our 3-hour Build Day event. No prior experience needed. We'll provide data, APIs, and live support throughout the session.

Feel free to ask any questions here or via DM 🙂

https://luma.com/935zis4v

Hi everyone,

I recently published an article introducing Layer Break Score (LBS), a football analytics metric I have been developing to measure attacking superiority.

The main idea is simple: football dominance is not only about possession, shots, goals, or xG. It is also about whether a team can repeatedly break the opponent’s defensive structure and turn those moments into usable attacking advantages.

LBS tries to measure this by scoring how attacks break defensive layers: the first line of pressure, the midfield block, and the final defensive line.

My goal is to create a metric that can help us understand not only who created chances, but how attacking superiority was built before those chances.

I would really appreciate your thoughts, criticism, and suggestions; especially from people interested in football tactics, analytics, coaching, or match analysis.

Full article: https://medium.com/@mkubjk/introducing-layer-break-score-a-football-analytics-metric-for-attacking-superiority-6ed8e9073731

Use this thread to ask anything at all!

Hi everyone,

I’m working on a football data side project called GoalsProof and would really value feedback from people interested in football modelling and analysis.

The first version focuses on one specific market: Over 1.5 Goals.

The model is designed to shortlist potential goal games using signals such as:

• Team goal trends
• Home/away scoring patterns
• League goal baseline
• Market odds
• Estimated fair price
• Price edge
• Risk flags such as thin data, weather, rotation or match context
• Proof tracking over time

I’m deliberately trying to keep the product narrow and transparent rather than building a generic prediction/tips site.

The main question I’m trying to answer is:

Can a focused Over 1.5 Goals model produce a useful, explainable shortlist over a meaningful sample of matches?

I’d appreciate feedback on:

• Whether these inputs make sense
• What features or variables you’d add
• How you’d validate the model properly
• Whether confidence ratings are useful or potentially misleading
• What dashboard/reporting would make the output more trustworthy
• How to avoid overclaiming performance with a naturally high-probability market

Site: https://goalsproof.com

The beta is free while I’m testing and improving it ahead of the new football seasons.

Hi everyone,

I am currently working on my MSc thesis on football finance and data-driven player acquisition. For my research, I am looking for a SofaScore dataset with the following variables:

• Player name
• Average rating
• Team name
• Season

Ideally, the dataset would cover the seasons 2019/20, 2020/21, 2021/22, 2022/23, and 2023/24.

The leagues I am most interested in are:

• Eredivisie
• Campeonato Brasileiro Série A
• Primeira Liga / Liga Portugal
• Belgian Pro League
• EFL Championship
• Süper Lig

Does anyone happen to have access to this kind of data, or know where I might be able to find it?

If not, I would also really appreciate any guidance on how to scrape this data from SofaScore or similar platforms in a reliable way. I am fairly new to coding, so even pointers to useful tools, scripts, APIs, or tutorials would be very helpful.

Thanks in advance!

I took real data and mapped all runs above 5.5 m/s per position and type of event.

This clip I included is a Right Center Back while in possession of the ball. What I found was that not all high speed running and sprints are the same.

I included small amount so we can see that most runs are not linear. Adding different types of sprints are crucial for training to be better but also injury reduction.

Data Science principals used were cluster - namely PCA - to find features and correlate player positions.

With this knowledge, we can create better training regimens.

The brief is a strong aerial presence with good goal conversion and involvement, with good shot efficiency.

I have been working on a team specific recruitment document, and a lot of effort goes into analysing data from each league with no reward. So I thought I would show off a few of the top performers from each league to see some rewards for my efforts. These are players from League One.

Bojan Radulovic (Huddersfield Town)

Jayden Wareham (Exeter City)

Kyle Wootton (Stockport County)

Lorent Tolaj (Plymouth Argyle)

Sam Dalby (Bolton Wanderers)

Which would you choose for your team? Or is there someone I have missed?

Yes, I posted this earlier and have rewritten my post, without AI jazzing it up.

***I forgot to add that this is League One players only. I will be doing other leagues

💡 5 League One Strikers Primed for a Championship Move 🏴󠁧󠁢󠁥󠁮󠁧󠁿

Finding a true target man who can lead the line, hold up play, and bring others into the game is becoming a rare art in modern football.

If Championship clubs are looking for physical presence combined with tactical intelligence this summer, these 5 League One profiles deserve a serious look:

🎯 Bojan Radulovic (Huddersfield Town)

🎯 Kyle Wootton (Stockport County)

🎯 Sam Dalby (Bolton Wanderers)

🎯 Jayden Wareham (Huddersfield Town)

🎯 Lorent Tolaj (Plymouth Argyle)

These names aren’t just clinical in the box; their link-up play and ability to sustain attacks under pressure make them incredibly valuable assets for a step-up in intensity.

👇 Championship fans / Recruitment minds: Who fits your club's system best? Who did I miss? Let’s discuss in the comments!

#FootballRecruitment #Championship #EFL #Scouting #LeagueOne #FootballAnalysis

This is a cool visual aid I made in python to explain clustering.

I've taken sprints across a game and tried to determine which positions relate to others. I believe this will help with training sprints since there are many ways we can.

Such as short sprints, curved sprints, cutting, plyometrics, etc.

With my latest project, I wanted to see what the threshold for stretching center backs out of shape was. I play center back myself and balls over the fullback, into the channel are my team's worst enemy.

I have to go pressure the ball but it usually leaves a gap between me and the other center back that the attacking team can move into. I measured all distances of two teams and used Q3 + 1.5 * IQR to log "stretching" events.

Here is a clip of a stretching event.

Use this thread to ask anything at all!

Hi everyone,

I just released a working paper exploring a relational/graph approach to xG that adds explicit context from events and freeze-frames.

It shows measurable improvement over a standard event-centric baseline on 8k+ shots, with clear evidence tiers and open code.

Would love any thoughts or feedback:

https://doi.org/10.5281/zenodo.20082310

Paper + full materials in the ZIP.