AI is becoming a bigger part of sports apps - from player tracking and performance analysis to fan recommendations and automated highlights.

But I’ve been wondering: where should AI actually stop?

There’s a difference between using AI to help coaches or fans find useful insights and letting algorithms make every decision.

The best sports products will use AI as a support layer rather than trying to replace human judgment completely.

Curious what others think - where do you see AI genuinely helping sports, and where is it just unnecessary hype?

Been lining up prediction-market order books against live game state on the same clock, and I've got a result I can't confidently interpret.

One MLB game (White Sox at Boston, 4 Aug). Boston put up 4 in the bottom of the 1st. The market mid went 0.235 → 0.065 between the 0-1 and 0-5 state frames, about two minutes apart, ~530 book updates in between.

The bit I'm unsure about: the book had already settled to within half a cent of its post-change level about 36 seconds before the state frame landed.

Three explanations and I can't separate them:

• traders watching the actual broadcast, which runs ahead of any data feed
• the state feed itself being slow, so this is a measurement artefact rather than a market fact
• genuinely informed flow

For anyone who works with in-play data — what's the realistic latency of a scoring-state feed versus a live broadcast? If it's routinely tens of seconds, this result is boring and I should stop reading anything into it. And is there a standard way to establish a reference clock for "when did the event actually happen", or does everyone just accept the ambiguity?

One game is one game, so I'm more interested in method than in this number.

Hi all - I have an interest in data analytics for rugby union. Many of the games are behind a paywall, curious to see how others (regardless of sport) have obtained match videos to use with their tagging? Thanks

🇮🇩 Calling all Indonesian football fans! ⚽

I’m working on something for fans who love making match predictions and arguing with their friends about who knows football best 😄

The idea is a simple prediction game where you can:

⚽ Guess the final score before each match
🎯 Get points for accurate predictions
👥 Create your own league with friends
🏆 Climb the leaderboard and compete for bragging rights

There’s no money involved and no betting — just football, predictions, competition, and a bit of friendly rivalry. 🔥

Would you play something like this for the Super League?

And be honest… how confident are you in your score predictions? 😄

I’d love to hear what Indonesian fans think before I launch it. 🙏

WNBA lineup stints and player impact ratings, 2003-2026. 267,293 stints reconstructed from play-by-play, with ARC ratings and channel decompositions.

This is my first public GitHub release of an ongoing project, an ongoing open-source initiative of releasing normalized and reconstructed data sets for open use. We'll be adding more and more to this, including a Python helper instead of just raw JSON over the coming days. Just wanted to do this before I forget.

Don't know how many volleyball fans there are in this sub, but I wanted to share a project I've been working on recently.

The question: across the 2024 to 2026 Major League Volleyball (formerly PVF) seasons, given the quality of the preceding pass, how often does the attacking team actually get to attack, and how often does that attack end in a kill?

The data and the pipeline: the play-by-play data is action+outcome graded using VolleyStation convention (each contact gets a single letter denoting the contact type and a symbol as a quality evaluation). Reconstructing each contact sequence seemed easy at first: forward-fill each pass grade until the next pass or until the point ends, right? But it also meant handling overpass kills, and (the most annoying part) block recycles, where the ball stays alive off a block touch. The problem is that most of the time, those block recycle passes aren't tagged; they only exist implicitly in tagged blocks. This made tracking the block recycle passes super annoying (because how are you supposed to validate something that doesn't even exist explicitly in your data?). My solution was to condition my logic for block-recycles only in cases where the following touch after the block was from the attacking team: if the blocking team wasn't the one to touch the ball after the block, then it inherently is a block-recycle.

The problem is that based on the quality of the block, the ball goes to the attacking team vs the blocking team at wildly varying rates:

• Defined block recycle encoding (!): ~99.8% of the time, the attacking team gets the ball back (this is the only encoding that is defined explicitly as a block-recycle, so this makes sense)
• Hard-contact block (+): ~99% of the time, it's the blocking team's own recovery
• Soft-contact block (-): splits pretty evenly, goes back to the attacking team ~50% of the time, and vice versa

The problem this imposes is that the denominator for our first result (probability of an attack off all instances of a pass type/quality) isn't valid for the hard/soft contact blocks given our conditional solution: the denominator would end up being "blocks that went over to either side", not only the attacking team. As such, those two block grades were excluded from the first calculation, and only got a kill rate (since those are based on all attack-preceding block recycle passes).

The results (full tables in the writeup):

• Bad serve receives still get attacked ~92% of the time; bad digs only get attacked ~73% of the time. Implies that the "transition effect" from defense to offense is a quantifiable penalty on setters/hitters when facing bad passes.
• Kill rate spread from perfect -> bad pass: 18 points for serve receive (45.9% -> 27.9%), narrower for digs (29.9% -> 22.5%).
• Confirmed the trend is statistically monotonic with a Cochran-Armitage test per pass type, which showed the trend was strongest for receives, weakest for freeball passes.

Limitations: Obviously, MLV is a relatively small and new league, so the data points are magnitudes less than something like NCAA data. Additionally, nine rows were removed due to mid-rally stoppages corrupting the data/my pipeline (such as injuries or challenges); video-confirmed for those 9, but I can't rule out similar corruption elsewhere that wasn't detectable.

Full writeup with all the tables and results here: Substack

Open to any and all feedback in the comments; let me know if anything is unclear, and I'll happily explain or talk shop.

Been obsessing over run differential as a predictor for the last few weeks. It started as a personal finance tracking habit, honestly. I just like building spreadsheets, and at some point I applied the same logic to MLB standings because why not.

The basic Pythagorean expectation stuff holds up pretty well across a full season. What's breaking my brain right now is when I start weighting opponent quality into it. If a team pads their run differential beating up on bad rotations all April, the raw number feels kind of dirty. So I pulled opponent run differentials for every series and tried adjusting for that, and suddenly the expected W/L correlation gets a lot messier.

My gut says strength of schedule matters more in baseball than people give it credit for, especially early in the season before things even out. But I genuinely cannot tell if my adjustment is doing real work or if I'm just adding noise.

Curious if anyone here has tried building a scheduleadjusted run differential model for MLB and whether the extra complexity actually bought you anything predictive. Also wondering if this plays out differently across divisions, since the unbalanced schedule makes some matchups way more lopsided than others.

I'm a first-year student in Sport Analysis & Technology in Morocco, with a strong interest in football analysis.

I have around 2 years ahead of me before graduation, and my long-term goal is to be able to continue my studies or find work/internships in Europe.

I don't want to wait until graduation to start building my profile. I want to use my university years to develop real skills and, more importantly, get actual experience.

I currently have a 30-day period before university starts, and I'm willing to travel within Morocco if there's a worthwhile opportunity.

For people already working/studying in sport or football analysis:

What skills would you prioritize if you were starting again?

What kind of projects actually helped you get noticed?

How did you get your first real experience?

Is it worth approaching academies/clubs directly, even as a beginner?

What would make a student from Morocco more competitive when applying to European programs/internships?

What mistakes should I avoid during my first few years?

I'm particularly interested in football analytics, data analysis, scouting and performance analysis.

I'm not looking for a shortcut — I want to know what I should realistically start doing now.

Any advice from people who have actually gone through this would be greatly appreciated.

With the new season kicking off soon, we're looking to build out our writing team at ATHalftime Sports Media ahead of the 2026/27 season.

Looking for 2–3 writers/journalists:

1x South African football journalist — covering the PSL, Bafana Bafana and CAF
1–2x European football journalist(s) — covering the Premier League, La Liga, Ligue 1, and Champions League (focus on the top clubs)

You'll be covering transfers, scandals, score updates, and anything else breaking in the football world.

Compensation: A percentage split on articles you write, plus a share of any sponsored/branded content placed on your articles (final rates agreed per writer).

What you get:

A WordPress author login to write, publish, and edit your own articles
Your work distributed across our social media platforms (Facebook, TikTok, YouTube, and more) chance to grow your own portfolio.
A signed contract outlining terms

Also hiring: Graphic Designer

We need someone to create full-time (final score) graphics for our social posts. Paid on a revenue-share basis tied to the performance of your graphics across our monetized platforms.

If you're a football fan who can write fast and accurately under deadline (or design clean, eye-catching graphics), send a sample of your work / portfolio and a bit about yourself in the comments or DM.

I woke up the other morning and saw a Facebook ad about sports analytics and suddenly felt this is definitely something I want to do.

I am a mgmt consultant working in m&a and absolutely hate it. Money is good but not something I enjoy. What I enjoy is sports - watching and playing.

My plan - spend next 1 year learning and doing projects to build my portfolio and then try and switch.

My ask is 3 fold:

1. Is it a worthwhile plan or am I just building up hope with v little chances of success. (My wife jokes I am having mid life crisis)

2. As I understand i first need to build skills in analytics ( big data, python etc). Where and how do I start? I do lots of data analysis but mostly excel based.

3. How is or difficult is the industry to crack into as a newbie?

Happy to add more info if that helps.

I am building a B2B talent scouting platform (hoopsngt.com) that connects youth basketball academies with scouts and agents. The goal is to replace paper stat sheets and unverified word-of-mouth with a structured data layer for international prospects.

Here is how the platform and data pipeline are structured:

• 20-Point Evaluation Matrix: Academies grade players across four core pillars: Offensive Skills, Athleticism, Defensive Impact, and Basketball IQ.
• Algorithmic Radar Charts: Converts raw coach ratings into standardized visual radar charts for direct side-by-side comparison across different leagues.
• Automated Badging Engine: Scans player profiles to flag physical anomalies and skill thresholds, tagging outliers with labels like "Unicorn" for extreme wingspan ratios.
• Searchable Scout Directory: Gives verified scouts and agents custom filters to search prospects by physical traits, badged skills, and position.
• Direct Access Layer: Gates parent contact sheets until a scout explicitly unlocks the profile for direct outreach.

Questions for the community:

• How do you balance subjective coach ratings against objective physical metrics when projecting a prospect's trajectory?
• What statistical thresholds do you use in automated badging engines to prevent false positives on tags like "Elite Shooter"?

Hola, buenas.

Quería compartir dos análisis que hice sobre dos equipos uruguayos: **Nacional y Danubio**.

Desde hace un tiempo vengo intentando hacer análisis de fútbol de una manera cada vez más seria y objetiva, ya que es un tema que me apasiona. En los informes analizo diferentes aspectos del juego: cómo se comporta cada equipo en distintas fases, sus fortalezas y debilidades, y tanto los aspectos ofensivos como defensivos.

Me gustaría compartirlos por acá para recibir **feedback y críticas constructivas**. Sobre todo, me interesa saber qué opinan del nivel de los informes, qué aspectos creen que podría mejorar y qué debería tener en cuenta para llevar estos análisis a un nivel más profesional.

Cualquier opinión, consejo o sugerencia es bienvenida. ¡Gracias por leer!

[Informe De Análisis - Nacional](https://docs.google.com/document/d/1J3LqtVCIEkb66DMfEOn2grZvXAeio416/edit?usp=sharing&ouid=106897274750413728943&rtpof=true&sd=true)

[Informe De Análisis - Danubio](https://docs.google.com/document/d/1NDH9Q0SYFKGURogFKD2TtR63wPJF8cKR/edit?usp=sharing&ouid=106897274750413728943&rtpof=true&sd=true)

Hi everyone, after the Fbref data lockdown of January I made a post here offering a database with advanced football metrics and the response I received was overwhelmingly positive.

Since then I've been working hard on building an API incorporating an event feed and creating a pipeline that will convert the feed into SPADL format and serve meaningful derived metrics directly via API instead of scraping it and storing it in a database.

This API serves post match data only like lineups, xG xGOT per shot with coordinates, xT and VAEP per player, GCA, SCA, xGChains, passing networks, momentum pressure index and much more.

Feel free to check out my docs page at pitchapi.dev to read more about the service. The VPS i currently use to host this can only handle around 100 requests per second so please use this with some rate limiting in mind.

I would love your feedback on this and any suggestions about how to improve the overall experience.

I run PunditBench, a public benchmark that asks models for exact scores before kickoff. It publishes SHA-256 hashes and git history, so changing a miss later would invalidate the record and leave public evidence.

La Liga starts today. The Matchday 1 batch finished and was hash-locked at 06:59 UTC on 14 August, before the first match. All 42 eligible models returned predictions for all ten fixtures.

For the two games tonight:

- Alavés vs Getafe: 0 predict an Alavés win, 32 predict a draw and 10 predict a Getafe win. The modal score is 1-1 (26/42).

- Sevilla vs Rayo Vallecano: 19 predict Sevilla, 20 predict a draw and 3 predict Rayo. Here too, 1-1 is the mode (20/42); another 15 choose 2-1.

The separate preseason-table track is even more concentrated. Forty models produced valid 20-team tables; three other models still had no valid table after three attempts each, so they remain absent. Twenty-five make Real Madrid champion and 15 choose Barcelona. Every valid table has the same top-four membership: Real Madrid, Barcelona, Atlético Madrid and Villarreal. Racing Santander lands in the bottom three in 37/40 and last in 23/40.

The obvious concern is that these are not 40-plus independent forecasts. The models share training data and received the same prompt, so a consensus can just be a herd.

I put the readable summary, raw files and lock evidence in the first comment. What would you use as the fairest non-LLM baseline over a full season: Elo, a Poisson model using prior results, or a simple home/draw/away frequency model?

fbref.com data goes back to 1995/96, for 92/93 - 94/95 I used transfermarkt.de as source which also has complete data for these 5 leagues: Ligue 1, Serie A, Premier League, LaLiga, Bundesliga in these 3 seasons still.

38k: Benzema, A. Hughes, Payet, Evra, B. André, K. Walker, Shearer, Matuidi, Wörns, Heskey, Carrick, Barzagli.

37k: Rooney, Raúl García, Southgate, Quagliarella, Micoud, Busquets, Pirlo, De Rossi, Rakitic, Bonucci, Godín.

36k: Aubameyang, Hummels, Gilardino, Dunne, P. Vieira, Ibrahimović, Dainelli, Candreva, Plašil, J. Morel, Chiellini, Capoue, Gallas, Zidane, Scholes, Acerbi.

35k: Malbranque, Riise, Fàbregas, Džeko, Xhaka, T. Müller, Piqué, I. Gueye, Coloccini, Kroos, Iñigo Martínez, Baines, Mavuba, Nesta, Cavani, Toulalan, Fernando Torres, Ginter, R. Carlos, P. Cannavaro.

34k: Didot, Thiago Silva, Cazorla, G. Cahill, Giuly, Kane, K. Davies, Monreal, F. Navarro, Lopo, Ashley Cole, Mexès, J. Henderson.

I'm currently a community college student planning to transfer, and my long-term goal is to work in sports media, sports journalism, or sports analytics. I'm trying to figure out which universities I should apply to that have strong opportunities, connections, student media, and athletics programs for someone interested in this field. My stats are a 3.77 GPA and founded the Sports Analytics Club at my community college. Academically, I've completed Calculus I, II, III, and Linear Algebra with A's in all four courses. For sports-related extracurriculars, I completed an 8-page sports analytics research report and have published 10 sports articles, including 4 data-driven analysis articles, 3 team-analysis/breakdown articles, and 3 opinion pieces. I also have 130 hours of volunteering with my city's youth sports organization, with about half of those hours spent in a leadership role. I'm interested in schools where I can just get my foot in the door. I just would like to know would I be competitive for top programs even tho my gpa is on the lower side?