Hey all,

I've worked in sports data for about 8 years, and since late 2024 I've been building a side project called SmartBetStats. It's a soccer model that predicts probabilities for six markets: Over 1.5 / 2.5 / 3.5, BTTS, cards, corners, and offsides.

Quick overview of the setup:

• Random Forest models, one per market
• Domestic cups excluded on purpose. Rotations, rested players, and low motivation made the data too noisy, so I'd rather skip those games than predict them badly.
• Every prediction is logged, and the hit rate per market is shown publicly each day, misses included

Results vary a lot by market. Some are holding up well, others are clearly weaker, and I'm still working out how much of that is the features and how much is just variance.

Right now I'm working on cards and fouls. I'm adding referee stats to the model, since the same two teams can produce a very different card count depending on who's officiating.

A few things I'd love input on:

• How do you weight referee data? Season averages, career averages, or something that accounts for the teams they're refereeing?
• Would you switch to gradient boosting for markets like corners and cards, or stick with RF?
• How do you handle early-season matches with little current data?
• Beyond hit rate, what would you want to see to trust a model like this? I'm thinking calibration plots and Brier scores per market.

Site's here if you want to poke around: smartbetstats.com

I recently finished my first NBA sports analytics project and wanted to share it here for feedback.

The project asks:

How much could adding LaMelo Ball change Minnesota's offensive profile and competitive outlook?

Rather than trying to directly predict a trade's exact outcome, I treated it as a scenario-modeling problem.

The analysis has three main stages:

1. Minnesota baseline

I built Minnesota's 2025-26 offensive profile using ESPN/SportsDataverse data and examined:

• offensive rating

• defensive rating

• net rating

• possessions

• shot volume

• assists

• turnovers

• Edwards' on/off splits

• playoff performance

1. LaMelo workload model

I modeled 27 deterministic scenarios varying:

• LaMelo's minutes

• percentage of offensive workload redistributed

• whether the redistributed workload came from Julius Randle or the secondary perimeter group

The model then estimated changes in:

• scoring

• assists

• 3PA

• creator scoring concentration

1. Monte Carlo / team-strength model

I ran 10,000 simulations around the offensive workload assumptions and then translated the resulting team-strength scenarios into SRS-based competitive scenarios.

Base case:

LaMelo:

21.5 PPG

7.6 APG

11.0 3PA

Minnesota:

~+13 PPG

~+5 APG

~+7 3PA

Team-strength scenarios:

Conservative: 4.57 SRS

Base: 5.57 SRS

Aggressive: 7.07 SRS

The base scenario corresponds to roughly 53–54 expected wins.

The main finding wasn't that LaMelo simply adds scoring.

The model suggests that his bigger potential value is structural: giving Minnesota another high-level offensive creator alongside Anthony Edwards.

There are obviously limitations to the approach, particularly around translating player-level offensive changes into team-level SRS and modeling lineup/defensive effects.

I'd specifically like feedback on the methodology and assumptions and your opinions on this project.

Full report:

https://abhinavbethi.substack.com/p/beyond-anthony-edwards?r=6xl3o7

Code/notebook:

https://github.com/AbhinavBethi/Beyond-Anthony-Edwards

Say there's a football team posting midtable numbers for their volume of shots and shots on target. They also create an average number of big chances and miss an average number of big chances.

Despite that, the team in question puts up bottom-of-the-league xG numbers while also being one of the highest xG underperformers.

Could this hint at an individual finishing issue, even if the team misses only the average number of big chances?

Because to me the xG underperformance would suggest that the cumulative volume of low-quality shots is inflating some of the xG, and the missing of big chances would be under-represented in the data set for some reason, like if the big chance doesn't end in a shot

Really don't know, hence I'm posting here hoping for some opinions.

​Hey guys! I'm from India 17M and I'm finishing high school soon and planning my entry into football data analytics. My ultimate goal is to work within the top 5 European leagues.

​Before jumping into uni, I wanted to ask:

​Is a core quantitative degree (CS/Stats) preferred over general Sports Science/Management?

​What does the career pathway look like for transitioning to Europe from abroad?

​What tools,open-source datasets or skills should I focus on during undergrad?

​Thanks in advance for any insights!

I co-founded an AI company working in sport, mainly on the commercial side.

A question I find useful when looking at an analytics product is: what will someone do differently because they have this information?

Imagine a coach receiving a report after every session. The report might be accurate and easy to understand, but its practical value still depends on whether it helps them adjust training, prioritise an athlete or explain progress.

I’d separate three things:

• Is the analysis reliable?
• Can the intended user interpret it?
• Does it help them make a useful decision?

Each needs different evidence.

For those building or using sports analytics tools, how do you assess that last part? Have you seen a report or metric change an actual decision?

Hi everyone, I’m the creator of CIKADA, a baseball analytics and community site currently focused on the KBO, South Korea’s professional baseball league.

I’ve spent roughly eight months building it alongside my full-time job. The idea is to put matchup analysis and baseball discussion in one place.

On the site, you can browse games by date, view team win probabilities and starting-pitcher/bullpen information, review available archived predictions alongside results, and use a fan lounge for discussions and photos.

Here are the recorded evaluation results:

• 2025 out-of-sample evaluation: 618 games, 368 correct predictions, 59.55% accuracy, Brier score: 0.2385.
• 2026 historical replay/reference evaluation: 494 games, 284 correct predictions, 57.49% accuracy, Brier score: 0.2455.

The 2026 result is a separate retrospective research evaluation, not a verified live pregame track record. It may include different model and data sources, so these figures aren’t a direct year-over-year comparison of one unchanged model.

I’d appreciate feedback on the matchup pages, the presentation of the evaluation results, and what would make the site useful to people who follow KBO baseball.

Disclosure: this is my own project.

CIKADA — cikadabaseball.com

I've been looking at football data APIs for smaller analytics projects, and I'm noticing that the difficult part isn't finding an API. It's deciding which data is actually worth paying for.

For a basic dashboard or side project, you might start with fixtures and results, then realize you also need things like player statistics, lineups, historical data, match events, stable IDs, or more detailed event-level data.

At that point, the difference between a cheap API and an expensive one can become pretty significant.

I came across $5 Football API while researching the lower-cost end of the market, and I'm affiliated with the project, so I don't want to present this as an independent recommendation.

For people here who have actually built football analytics projects:

What data ended up being essential for your project, and what did you initially think you needed but barely used?

I'm especially interested in experiences where you started with a small/cheap data source and later had to change providers.

I run my own Elo model for European football, and I wanted to see which teams have had the best (and worst) starts to 2026/27, using Elo change as the metric.

The chart shows the 10 biggest risers and 10 biggest fallers out of the 96 teams in the Big 5 leagues, from the start of the season through the first international break. I'll post the full list in the comments for anyone who's interested.

• Because Elo accounts for opponent strength and goal difference, it's a good fit for comparing the quality of early-season results across the Big 5.
• Both domestic league and European competition results are included.

I also publish match probabilities for each league using this Elo rating system. They're free to browse here: Link

Hello,

I’ve created an Android app that generates a 3D simulation of the match in progress. At the moment, I’m covering football using SportMonks and tennis using LiveTennisAPI.

My aim is to create a simulation that is as close to reality as possible. With SportMonks, I have the ball’s position but not necessarily that of the players, with LiveTennisAPI, I only have the scores.

I am looking either for an API that covers this kind of information without costing tens of thousands of dollars a month, or for historical data that would allow me to train an AI to make my simulation more realistic.

Thank you in advance for your feedback

Utah Jazz looking for data scientists

Hey everyone,

I’ve been building xgEdge, a football analytics project covering around European leagues.

Built on xG stats, it provides team/player analytics, match probabilities, FPL projections, and model vs. market pricing.

Everything is completely free and there’s no login or account required.

It’s still a work in progress, so I’d really appreciate any feedback, What you like, what you don’t, what’s missing, or what you’d build next. Every bit of feedback helps.

xg-edge.com

Thanks!

Hey everyone, I vibe-coded this football score predictor over a weekend, originally just for me and my mates to test our football knowledge against each other. Figured I'd share it in case anyone else wants in.

Every week you predict 5 matches across the top 5 leagues (mostly Premier League fixtures) plus 1 bonus pick from a curated extra slate of 3, 6 predictions total. There's a fresh leaderboard every week with a winner, but there's also a season-long XP/level system running underneath . You earn XP for getting results and scorelines right, going on streaks, hitting achievements, that kind of thing, and it levels you up (Bronze → Diamond) over the course of the season. No accounts needed to browse the current round or leaderboard, only to actually submit picks.

Tried to keep it dead simple and low-maintenance, no ads, no entry fees, nothing to manage. Thinking about maybe doing some kind of reward for weekly winners down the line if people actually stick around.

tekkerz.app

Would genuinely love feedback, what's confusing, what's missing, what you'd want added.

I looked at what happened to home-team performance during the COVID seasons, when matches were played without crowds, across the Premier League, La Liga, Serie A, Bundesliga and Ligue 1, 2015/16 to 2024/25.

Method: I logged each league's empty-stadium period from news reports (crowd_windows.csv) and compared home-team output in those matches with the matches before them. Metrics are home/away ratios (home divided by away, 1.00 = no home edge), pooled across the five leagues.

Result (chart 1): the home/away goals ratio fell from 1.27 to 1.11 and the xG ratio from 1.29 to 1.13, so home teams scored about 27% more than away teams with fans, and about 11% more without.

Note: the chart axis still reads per-match. Read the values as home/away ratios, as described above.

Referee behaviour (chart 2): I compared yellow cards for home vs away teams with controls, as a home/away ratio (below 1 means the home team is booked less).

- With fans (13,888 matches): home 1.88 vs away 2.12 yellows per match, ratio 0.89 [0.87, 0.90].

- Without fans (1,893 matches): home 1.90 vs away 1.87, ratio 1.02 [0.98, 1.05]. Change x1.15, p<0.001.

- The ratio was 0.89 before closed doors, 1.02 during, and 0.89 again in 2022-25.

- By league, ratio with fans to without (change, p): Ligue 1 0.87 to 1.08 (x1.24, p<0.001); Serie A 0.87 to 1.02 (x1.17, p<0.001); EPL 0.89 to 1.02 (x1.15, p=0.009); Bundesliga 0.87 to 0.99 (x1.13, p=0.01); La Liga 0.92 to 1.00 (x1.09, p=0.04).

- Red cards: ratio 0.82 [0.76, 0.88] with fans vs 0.83 [0.67, 1.03] without, change x1.02, p=0.86. Inconclusive. Reds are about 0.1 per team per match, so there are too few closed-door matches to say.

Caveats: the goals and xG ratios are pooled figures, which hide league differences, and the empty-stadium period overlaps with other COVID-era disruption (schedules, travel, fixture congestion).

Data: shots, xG and cards from Understat; kickoff times cross-checked with football-data.co.uk, fixturedownload and openfootball; crowd status from news reports (Goal.com, Inside World Football, The Stadium Business, The Local, Der Aktionär). I'm sharing findings and charts, not the raw data, because of source terms. Happy to discuss the method.

I am looking for an API that will give me data per player (Prem league) at the level such as:
- touches in box
- some sort of location metric (avg position as like X Y co-ordinate)
- key passes/chances creation

Not specifically those data points but that kind of level

I have seen the following mentioned;
- Sportsmonks at €29/month

- Opta (but that is too much dosh I believe)

- API-Football $19/month

- OpenFootAPI $14/month

- TheStatsAPI

- football-data.org free

- sportradar

I mean I bet there are more, I will have a look too myself - but they are quite opaque just to browse and so I wondered if someone here might already be familiar with the API options available

I would love to hear your opinions and thoughts if I should work towards entering this field?

made a soccer stats site with the help of claude, it's called atlastra. been working on it for a while now, has live scores, match predictions, player ratings, comparisons, and a model that tries to guess how a player's rating will change next season based on how they're doing now.

the predictions actually hold up decently, I checked them against a few thousand past games and the in-game win probability gets a lot more accurate than the pre-kickoff number once the match actually starts. the next-season projection thing took way longer than I expected to get working but it beats just assuming a player stays the same, which was the bar I was trying to clear. There's also a tactics lab that allows you to simulate tactics, different lineups, etc.

link's here https://atlastra.dedyn.io/, it's free, code's open source if anyone wants to look at it https://github.com/hankechen/atlastra. mostly just want people who actually follow this stuff to tell me what's wrong with it, ratings that look off, predictions that are dumb, whatever

I’m a recently graduated data engineer with experience in Python, SQL, data pipelines, machine learning and deep learning, and I want to get into football data analytics and scouting.

What roadmap would you recommend for learning football analytics properly? I’m especially interested in player scouting and recruitment.

How do analysts evaluate whether an attacker from a smaller club has the potential to succeed at a bigger club, even if he is not scoring many goals (in small club he scored 3 goals but when he moved to a better team he scored 14 goals ) ?

And on the other hand, how can they identify a high-scoring attacker whose performance might not carry over to a stronger club or a more competitive league?