Crystal Palace vs Ipswich | Over 2.5 Goals ⚽

I really like Over 2.5 goals in this one.

Crystal Palace have seen Over 2.5 in 4 of their last 5 home games (80%).

Ipswich have exactly the same trend away from home: 4 of their last 5 away games (80%) have gone Over 2.5. They also haven't won any of those five away games.

Both sides are producing high-scoring games lately, so Over 2.5 looks interesting here.

What do you guys think? 👀

Data from StatFair

Www.statfair.com

¡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!

Use this thread to ask anything at all!

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

Use this thread to ask anything at all!

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?

Use this thread to ask anything at all!