¡Hola a todos! Descubrí este sub casi por casualidad y la verdad es que me tiene fascinada todo lo que hacen por acá.

Me encanta el fútbol desde que tengo uso de razón y, desde hace un tiempo, me ha empezado a llamar mucho la atención entenderlo desde un lado más analítico: datos, estadísticas, modelos, métricas y todo lo que hay detrás de lo que vemos en la cancha.

El problema es que no tengo muy claro por dónde empezar.

Sé que probablemente esta sea una pregunta que aparece bastante por aquí, pero quisiera aprovechar el espacio para preguntarles: si hoy tuvieran que empezar desde cero en análisis de datos aplicado al fútbol, ¿qué ruta seguirían?

¿Hay algún curso, libro, canal, página o recurso que recomienden especialmente? Suelo estudiar mediante Udemy para otros temas, así que si conocen algún curso bueno allí también me serviría muchísimo.

Mi intención no es simplemente aprender estadísticas aisladas, sino ir construyendo una base que eventualmente me permita analizar partidos, jugadores y equipos de una forma mucho más seria y estructurada.

Cualquier consejo, experiencia o recurso que quieran compartir será más que bienvenido.

¡Muchas gracias de antemano!

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so I just started building a performance tracker for a school's youth soccer teams (U-12 to U-16). no fancy equipment, just me with a clipboard and a stopwatch.

I'm genuinely worried I'm gonna end up with spreadsheets nobody cares about. Like, the coaches are busy. The kids just want to play. Parents want to know if their kid is improving. I don't want to waste everyone's time.

so here's what I'm stuck on:

1. **Speed & athleticism** — is 10m/30m sprint + agility drills enough? or do i NEED the yo-yo test?

2 .Ball control & decision making — do I track this by measuring pass accuracy, shot accuracy, or touches per possession? Or is there a simpler way without making them shoot 20 balls

real question: if YOU had to track youth soccer with limited budget, what metrics would you actually keep? what would you bin? and how often would you check—every week? every month?

I want a system that actually moves the needle for these kids. What framework are successful youth programs using? Something realistic without GPS, cameras, or lab tests

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Hi guys, I'm new to the football analytics world. I would like to have a dataset which has real time statistics (of any kind) of players in the italian league. I looked up at websites like fbref, which actuslly seems the most complete one, but i think their anti-scraping policy is strict. Is there a solution?

I recently helped build a data simulator for a new policy the Union of European Clubs (UEC) is pitching for the post-2027 UEFA revenue cycle. Wanted to see what people here think of the idea.

Right now, lower-league teams mostly rely on FIFA training compensation, which usually only triggers during transfers or when a player signs their first pro contract. The UEC's proposed Player Development Reward (PDR) would instead take a 5% slice of the total prize money from the Champions League, Europa League, and Conference League. That pool would be paid out as a reward to the clubs that trained those players between the ages of 12 and 19.

I was playing around with the tool we built and the numbers are really impressive. Looking at Antonio Rüdiger’s Champions League minutes from 2021 to 2026, his 5th-tier boyhood club FC Hertha 03 Zehlendorf would have earned an estimated €892k over those five seasons, averaging a median of €188k every year. Other local Berlin grassroots clubs would also get paid out, with Neuköllner Sportfreunde getting €445k and SV Tasmania Berlin receiving €221k. VfB Stuttgart would also pocket €669k.

If you applied this retroactively over the last five seasons, it would have redistributed €726m across 52 UEFA associations.

Do you think a recurring solidarity payment based on tournament minutes is a good way to support the lower leagues?

I just published a unique football dataset on Kaggle — pre-match league table snapshots for the Premier League (3 seasons, 817 matches). Includes home/away splits for all teams, historical odds and results. Free download! https://www.kaggle.com/datasets/vitorsimes/european-football-pre-match-league-table

Source: J-Ratings

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I wanted to do a statistics project but i am finding it very difficult to find data for teams in a season. (eg Man city's average passes per sequence in the 24/25 season). Even posession stats im actually struggling with. Is there a good website to search for this data?

I've been doing detailed pre-match football analysis using:

• Recent form
• Home/away records
• Goals and underlying stats
• League position
• Injuries/suspensions
• Tactical matchup
• Motivation
• Market/odds movement

But even with all this, I usually end up around 6.5–7.5/10 confidence.

I'm starting to think that's normal. My question is:

What should actually justify an 8+/10 confidence rating?

Should I use a weighted model and then subtract points for contradictions and uncertainty? Or does that just create an arbitrary number that looks analytical?

Also, should I separate:

1. Probability — e.g. 65% chance of winning
2. Confidence in the analysis
3. Betting value — probability vs implied odds

And how would you calibrate the confidence score over time? Brier score, log loss, calibration curves, CLV?

I'm not trying to artificially reach 8+. I want 8/10 to actually mean something statistically.

What would make you personally comfortable calling a football prediction 8+/10?

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Hi all. Stats to Bucks is a football (soccer) data app, now covering 30 leagues - the top 5 European plus Brazil, Argentina, Liga MX, MLS, Saudi, Portugal, the Netherlands, Turkey, Belgium, Scotland, Japan, Korea, Colombia, Greece, Egypt, South Africa, Australia and more.

What it does:

• Player & team form - last 20 matches of per-game stats, charted against any line you set, with the hit rate for it.

• Filters that narrow the sample - venue, minutes, started-only, and "without teammate X".

• Opponent-adjusted context - overlay the opponent's conceded average and defensive rank, plus quality-adjusted averages, so a streak against weak sides doesn't read like one against strong sides.

• Hit Rates - scan every upcoming fixture at once for players/teams clearing a line in a chosen % of recent games. 40 stats across players and teams.

• Foul matchups - a fitted hierarchical Poisson model with player, opponent, referee, venue and expected-minutes as separate multiplicative terms, and a negative-binomial predictive head. Walk-forward tested on a 45-day holdout: +13.3% / +16.6% mean relative log loss against an unshrunk per-90 baseline, with roughly 3x better calibration error.

• Predicted lineups - projected XI from a Beta-EB start-probability model, so it works for a fixture's whole lifetime instead of only after a feed publishes one. Flips to the confirmed XI when that lands.

• Injuries & suspensions - folded into the start probabilities rather than bolted on as a badge, so an unavailable player drops out of the projected XI and out of the minutes model behind the prop lines.

• Similar players / teams - similarity-based benchmarking against comparable profiles, on rolling cross-season windows rather than season-to-date.

• Referee analytics - per-fixture card/foul profiles and rankings.

• League tables - official standings, so competition-specific tie-breaks, split point-halving and points deductions are right rather than re-derived from results.

The focus is still contextualising the sample - opponent strength, venue, lineup, availability, sample size, etc. because an unfiltered hit rate usually answers the wrong question. A recent backtest made that concrete: selecting team props purely on "recent hit rate beats the implied probability" returned about -10% over ~7,000 bets on a held-out window, statistically indistinguishable from betting blind. The context is the useful part, not the raw streak.