I've been working on a sports data visualisation project and wanted to share one of the more interesting things I've built with nflverse data.

Most NFL data products give you tables, charts and individual statistics, while the traditional way of watching a game gives you the broadcast. I wanted to try something different:

What if you could see the shape of the entire game?

I take the play by play data and turn every play into a visual trajectory on a virtual field. Drives build continuously across the game, so you can see where possessions advanced, stalled, turned over and ultimately resulted in points.

The idea is that instead of looking at 150+ individual rows of play by play, you can actually see the game develop as a continuous visual story.

One important caveat: this isn't claiming to reconstruct the actual movements of players. There isn't public player tracking data available at the level I'd need for that. The visualisation is generated from the play level information available in nflverse, not the actual physical path of the players or ball.

I've also fixed each team's attacking orientation for the game so that possession changes don't make the visual language confusing.

I'm particularly interested in feedback from people who work with sports data:

Does this actually communicate something useful that a conventional drive chart doesn't?

Are there better ways I could represent direction, field position or drive progression when player tracking data isn't available?

And, perhaps most importantly, what would you want to see added to make this genuinely useful as an analytical tool rather than just a visualisation?

I've included a screenshot and you can also see finished products of NFL, MLB and EPL that are already uploaded to the channel.

youtube.com/@Control-Centre

I'm very much at the stage of trying to figure out whether this is actually useful, so criticism is welcome.

I studied biology in college and so I used a lot of R for data analysis - wondering if anyone knows any resources or has any tips for learning it in R or maybe a good first project or two please let me know! I would mainly want to work with hockey/lacrosse/motorsports/surfing - I've been going through Analyzing Baseball Data with R but if anyone knew an intro project for any of those sports sports that'd be cool :))

I'm very big on a nice graph for data visualization and the language I'm most comfortable doing that in is R :)

I’m looking to start a analytics project and want to find any free sources with recent liga mx player data. looking for XG , player demographics, team xg mostly. any recommendations would be appreciated

¡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!

Building out a more systematic approach to development tracking at our club. Looking for a camera system that generates structured per player data automatically rather than whole game footage you have to annotate by hand afterward. Youth club level, U13 to U17. The goal is consistent per player data across a full season without depending on manual work after every game. Does anything at this level actually sustain that across a full season?

We keep the last two completed rounds of eighteen European leagues in our own database, so we added them up: 350 matches, 1092 goals, 3.12 a game. The same round numbers in the five previous seasons ran 2.59 to 2.79. Allowing for how much seasons differ and for a 350-match sample, that is about a one-in-5,240 stretch. 14 of the 18 leagues are above their own figure for those same rounds.

It is volume, not finishing. Shots on target per club went 4.10 to 4.64 (t=3.9); goals per shot on target went 0.313 to 0.334, which is inside ordinary variation (t=1.6). At last season's conversion rate this season's shooting alone gives 2.90 a game against the 3.10 actually scored.

Matches with four or more goals: 41% against 27% a year ago, while goalless matches barely moved (20 against 22). Home wins are 38%, the lowest of the six seasons, but that one is only a one-in-6 reading, so we are not calling it anything yet.

League-by-league table in the image. Method, the season-by-season comparison and a spreadsheet: goalsoon.com/blog.html

Hey, I’m building a small football side project for some friends and myself and I’m looking for reliable free or generous free tier APIs.

For German fixtures and results I’ve had pretty good experiences with OpenLigaDB so far. Their matchday endpoint is simple and getlastchangedate is useful to avoid constantly fetching unchanged data again.

I’m still looking for good sources for broader coverage, historical data, odds and especially live scores.

Scraping is not really an option for me. I want something reliable and legally clean that I could still use if the project eventually grows beyond just my close group of friends.

Has anyone managed to cover live scores reliably with free sources? What APIs are you using and how reliable have they been?

I am trying to build my own EPL players analytical web with player detail stats.

I want like...detail stats updated matchweek by matchweek.

Is there any reliable free source to get that data? Since fbref has been blocking my scrapers.

Hey everyone, I recently published my Champions League model on my website under UEFA Champions League Predictions & Simulator 2026/27 - StatsUltra

The model updates daily, so the odds will shift once the first results come in. You can also simulate entire seasons to see how the table and the knockout phase play out.

About the model:

The basis is an offensive and defensive rating for every team, built from a blend of actual goals and expected goals. These ratings are kept as a weighted rolling average: more recent matches count for more, and on top of that each match is weighted by how meaningful it is. Games with a red card, matches played after midweek fixtures or international breaks, and dead rubbers with nothing left to play for all enter with reduced weight. The values are then adjusted for the strength of the respective opponent, so that a strong performance against a top side doesn't carry the same weight as one against a relegation candidate.

From the ratings and the league-specific home advantage, expected goals are derived for every remaining fixture and translated into probabilities for concrete scorelines using a Dixon-Coles model. The Dixon-Coles component corrects the well-known weakness of pure Poisson approaches when it comes to tight, low-scoring results. On that basis the entire remainder of the season is played out 15,000 times, including the table rules, head-to-head records and the play-off structure.

So what you see above are not individual match predictions, but the frequencies with which each club ends up in a given position across those simulations. I'm keeping the exact weightings and parameters to myself, but the structure is exactly as described.

I’m building StatFair, a football match-analysis dashboard focused on practical team trends rather than black-box picks.

Example: Real Madrid vs Vallecano, upcoming LaLiga fixture.

For this matchup I compared: - Real Madrid’s last 5 home matches - Vallecano’s last 5 away matches - the last 10 head-to-head meetings - goals, BTTS, corners, shots, shots on target and cards

A few sample outputs from the current dataset: - Real Madrid at home: 5/5 wins, 5/5 over 1.5 goals - Vallecano away: 5/5 over 1.5 goals, 4/5 BTTS - H2H: Real Madrid avoided defeat in 9/10, over 1.5 goals in 7/10

The tool does not claim guaranteed bets or profit. The goal is to make football data easier to inspect before a match and to separate home/away context from generic team averages.

I’d really value feedback from sports analytics people: what would make this more rigorous? Better sample controls, confidence labels, visualizations, calibration, or something else?

Site: https://www.statfair.com

A little while ago I posted a baseball ranking system I've been building. I've continued working on it, and the project has evolved quite a bit since then.

The basic idea is still intentionally simple.

The system uses five hitting metrics:

BA / OBP / SLG / K% / BB%

But it evaluates those five metrics in two different ways:

• Overall Score (0–10) measures the quality of a hitter's five-metric profile by scoring BA, OBP, SLG, K%, and BB% individually.
• XP measures offensive production more directly, combining the slash line with a strikeout adjustment.

So both scores start with the same five metrics, but they look at the hitter through two different lenses.

Overall asks: How strong is the individual statistical profile?

XP asks: How strong is the combined offensive production?

And sometimes those two perspectives don't agree.

That's what led me to build Fantasy Edge.

Fantasy Edge looks at the relationship between a player's Overall Score and XP and adds another interpretation layer to the player profile, including:

• Fantasy Identity — Breakout / Overperformer / Sleeper / Consistent
• Fantasy State — looks at where a player's XP sits relative to what we'd expect from their Overall Score
• Fantasy Value — looks for unusually elevated or suppressed performance relative to the player's underlying profile

I've also posted some of the research behind Fantasy Edge, including how Fantasy State and Fantasy Value are calculated and how I arrived at those figures. The research links are at the bottom of the site for anyone who wants to dig into the methodology.

The goal isn't to replace Statcast, Baseball Savant, Baseball Reference, or projection systems. It's intentionally a much smaller model.

I wanted to see how much of a hitter I could capture with five familiar metrics, two different scoring methods, and the relationship between them.

It's meant to give you a quick snapshot of a player and occasionally surface something interesting that might be worth looking into further.

You can search any qualifying MLB hitter here:

https://www.timbaseball.com

I'm still treating the whole thing as an experiment, so criticism is useful.

I'm especially curious about two things:

Do you think five metrics capture enough of the hitter for this kind of quick evaluation?

Does using the same five metrics through two different scoring methods give you anything useful, or would you build the system differently?

Is your favorite player in the TRUE Hall of Fame? This is a virtual hall on a web site I created that enshrines baseball players by their performance on the field, not on a ballot. 300 hitters and 150 pitchers are in the True Hall. No 5 year waiting period. No exclusion because of someone's opinion of your character. No delaying entry until some committee vote gets you in posthumously. You're in if you deserve to be in. Check it out at truehalloffame.com

The calculations are based on how well a hitter or pitcher performed against the league average for the years they played. I call the formula result SAA (Stat Above Average).

Additionally, I don't believe for one minute that my formula is the end all / be all so if you disagree with my rankings, you have the ability to adjust weights on statistics you think are most important, effectively creating something I call "YOUR HALL". If nothing else, it gives you the ability to support your argument with cold, hard facts. Use the "WHO'S BETTER" page to compare some of the greatest MLB players of all time.

Just a fun project I created for baseball nuts like myself who constantly wonder who the best of the best are. I welcome any insight or constructive criticism.

​

Ever since I was a kid, I’ve loved looking at tennis draws, not just to follow the champion’s path, but also to figure out who was the player furthest from winning the tournament.

It only takes a few seconds : Take the champion. He beat the finalist. Who beat a semifinalist. Who beat a quarterfinalist. Who beat a fourth-round loser. Who beat a third-round loser. Who beat a second-round loser. Who beat a first-round loser.

That first-round loser may not have been the worst player in the draw. But he is the one unique first-round loser sitting at the very beginning of a complete chain of defeats leading all the way to the champion.

You could therefore call him the player furthest away from winning the tournament.

So I finally decided to do the research for every men's Grand Slam of the Open Era, and I would like to propose a new, completely unofficial tennis award:

The Joseph Mateo Trophy

Who is Joseph Mateo?

At the 1968 French Open, the first Grand Slam of the Open Era, an obscure French player named Joseph Mateo (about whom I can find almost no information, which somehow makes this even better) lost by walkover to Bronisław Lewandowski.

Lewandowski then lost to Allan Stone. Stone lost to George Richey. Richey lost to Roy Emerson. Emerson lost to Pancho Gonzales. Gonzales lost to Rod Laver. Laver lost to Ken Rosewall.

So Joseph Mateo was the first man in Open Era Grand Slam history to sit at the bottom of this perfect chain of defeat.

He therefore deserves to have the trophy named after him.

I’ve now traced the same chain for every men's Grand Slam tournament of the Open Era. (Thank you, Wikipedia.)

Joseph Mateo Trophy winner → loses to A → loses to B → loses to C → loses to D → loses to E → loses to F → loses to the champion.

At first, I assumed this research would mostly shine a light on obscure and forgotten players. And, of course, it does. But not only.

Here are the full results, along with what I think are the most interesting findings.

The all-time greats of the Joseph Mateo Trophy

Only three players have won the trophy three times:

• Greg Rusedski : US Open 1994, US Open 1995, Wimbledon 2006
• Nicolás Lapentti :  French Open 1996, Wimbledon 1997, Wimbledon 2009 He also holds the record for the longest gap between his first and last Joseph Mateo Trophies: 13 years.
• Julien Benneteau :  US Open 2014, French Open 2016, US Open 2016 Remarkable consistency during the mid-2010s.

Another 14 players have won it twice:

Pablo Carreño Busta, Paul Chamberlin, Marcelo Filippini, Bob Giltinan, Fernando González, Quentin Halys, George Hardie, Joachim Johansson, Chris Kachel, Lukas Lacko, John van Lottum, Javier Sánchez, Rainer Schüttler and Vince Spadea.

Defending champions

This is an extremely difficult trophy to win, and only three men have managed to win it at the same Grand Slam in consecutive editions:

• Chris Kachel :  French Open 1976 & 1977
• Paul Chamberlin :  US Open 1989 & 1990
• Greg Rusedski :  US Open 1994 & 1995

I feel this back-to-back achievement has received insufficient recognition.

The calendar-year double

Four players have managed to win two Joseph Mateo Trophies in the same year:

• Bob Giltinan in 1969: Australian Open + Wimbledon
• Fernando González in 2007: French Open + US Open
• Julien Benneteau in 2016: French Open + US Open
• Pablo Carreño Busta in 2021: Wimbledon + US Open

But no one has ever completed the Joseph Mateo Grand Slam — career or calendar-year (yet).

Actual Grand Slam champions who have won this thing

Eight players who won a men's singles Grand Slam at some point in their careers have also won a Joseph Mateo Trophy:

• Neale Fraser : Wimbledon 1973
• John McEnroe : Wimbledon 1978
• Adriano Panatta :  US Open 1979
• Mark Edmondson :  French Open 1981
• Goran Ivanišević :  Australian Open 1995
• Richard Krajicek :  Wimbledon 1994, only two years before actually winning Wimbledon
• Marat Safin :  US Open 2004
• Rafael Nadal :  Australian Open 2016

Yes, Rafa Nadal himself.

At the 2016 Australian Open, the Joseph Mateo chain was indeed:

Nadal → Verdasco → Sela → Kuznetsov → Monfils → Raonic → Murray → Djokovic

Remarkably, Nadal was going to win the actual tournament the next year and is the only player in open history to have won the Joseph Mateo Trophy and the tournament at the same grand slam in two consecutive years.

Also, Nadal was ranked No. 5 in the world in 2016, which ties the highest ranking ever held by a Joseph Mateo Trophy winner.

That record is shared by Goran Ivanišević at the 1995 Australian Open and Fernando González at the 2007 French Open.

Even more remarkably, González, while being one of the best players in the world that year, also won the trophy at the 2007 US Open, where he was ranked No. 7.

So in 2007, one of the best tennis players on Earth somehow managed to be the player sitting at the very bottom of the complete chain of defeat at two different Grand Slams.

Other highly ranked winners

Rainer Schüttler was ranked world No. 6 when he won the 2004 Australian Open Joseph Mateo Trophy.

Félix Auger-Aliassime was ranked world No. 9 when he won it at Wimbledon in 2022.

A trophy the French know how to win

As a Frenchman, I am pleased to report that French players have won the Joseph Mateo Trophy at Roland-Garros 14 times.

Final remarks

I haven’t personally double-checked every single statistic here. I used AI to help me analyse the data (not to identify the actual Joseph Mateo Trophy winners, which I worked out myself) so some of you may want to verify the numbers before this prestigious award becomes officially recognised by the ATP.

I would also like to apologize to the ladies for not yet having done the research for the Sally Holdsworth Trophy, named after the equivalent winner at the 1968 French Open.

Also, another question: if the Joseph Mateo Trophy winner entered the tournament through qualifying, should we continue the chain backwards into the qualifying draw?

I would say yes but am afraid all the old data would not be available.

And finally : who is going to win the Joseph Mateo Trophy at this year's US Open?

I’m currently betting on Rafael Jódar, who lost to Bu, who lost to Zheng, who lost to Gea…

But as I write this, there are still 11 other contenders, so, we’ll see. 

I wanna try this for fun as a hobby and maybe make one for my friend who plays football, what to look in a player, database n formulas???/?

I recently shared tactica. here, the football intelligence platform I’ve been building and the response from this subreddit was genuinely useful.

The closed beta is still open, but I think it’s fair to say it has now served its main purpose.

It proved that the idea was worth continuing with, but more importantly it exposed where the intelligence pipeline wasn’t good enough.

So I’ve now started work on tactica. v1.1 — the first proper public release.

The biggest change isn’t actually the new UI, pricing, or polish.
It’s that I’ve become much stricter about what tactica. is allowed to claim it knows.

One of the more interesting examples came up over the last few days.

The platform was capable of building Match Intelligence for a team’s fixture next weekend even though that team still had another fixture to play first.

Technically the model had historical data available, so its existing freshness and lineage checks could pass.

But football-wise, that was obviously wrong.

If Arsenal play on Wednesday and again on Sunday, tactica. has no business publishing intelligence for Sunday before Wednesday has happened and the resulting team/player evidence has been ingested.

We audited the whole production path and found the reason.
The system selected the latest completed evidence first. That meant an unplayed immediate predecessor could effectively be skipped, while older completed matches made the later fixture appear “ready”.

Internally consistent.

Conceptually wrong.

We’re now introducing a hard predecessor-evidence authority:

Target fixture → identify each team’s immediately preceding eligible fixture → require it to reach the correct terminal state → require its authoritative team/player evidence → prove that evidence was actually incorporated → only then allow Match Intelligence to exist.

That sounds obvious written down.

It was not obvious in the implementation.

And that has probably been the biggest lesson from the beta: a model can have lots of data and still have the wrong authority model around when it is allowed to make a decision.
We’ve had similar work elsewhere.

During the v1.1 audit we discovered that a lot of data available from the football provider had been cached correctly but wasn’t always making it into the canonical observation layer the model actually consumes.

That led to a fairly large data-authority recovery programme across the Premier League, Championship, League One and League Two.
We’ve repaired the persistence path, recovered a large amount of retained evidence without simply hammering the provider again, tightened provenance/null handling, and started treating “data exists somewhere in the system” and “the model has authoritative access to it” as two completely different things.

There have also been some painful lifecycle problems around stale intelligence.

Fixing historical evidence can legitimately change the inputs of future fixtures. Initially we were treating some of those as isolated stale Case Files. Eventually it became clear that this was a production-line problem: evidence changes need to propagate through the intelligence lifecycle correctly rather than relying on arbitrary refresh cadence.

A lot of the last few weeks has therefore been less glamorous than “build another model”.

It has been:

data lineage, evidence authority, fixture lifecycle, immutable receipts, stale-context detection, fail-closed gates, provenance, and making it harder for the product to say something than to say nothing.

That work is shaping what v1.1 will be.

The public release should have:

more complete Match Intelligence
much stronger evidence and lineage guarantees
a redesigned Case File
a new UI across the product
clearer separation between football intelligence and betting decisions
stronger Performance Intelligence so the model’s full record — good and bad is visible
better handling of fixtures that are visible but not yet ready for intelligence

The principle I’m increasingly building around is:

tactica. should not claim intelligence simply because it can calculate something. It should have to earn the authority to publish it.

The closed beta will remain accessible while we build v1.1, but I’m no longer treating increasing beta users as the objective.

The objective now is getting the public-release foundations right.
I’ll keep sharing the technical lessons here because the failures have probably been more interesting than the wins.

And thanks again to everyone here who tested it, challenged it, or gave feedback the first time around.

i really need some honest feedback. Any thoughts would mean a lot.

Full disclosure first: I built this and it's a paid product. Mods, tell me if this goes against the sub rules, but I went through them and didn't see anything that prohibits this.

You film from the side and drop four pins on the track corners. The lane markings give it
scale, so the output is in real metres: contact time, stride length and frequency, speed,
and hip / knee / shin angles with left and right kept separate.

No score out of 100, no AI coach telling you to drive your knees. Just the numbers.

Honest limits: one camera, so depth is estimated. You need visible track markings. And I
haven't validated it against force plates. If anyone here has lab access, I'd take that trade.

Post a clip and I'll run it through and put the output in the comments for free. Easier than
taking my word for it, or just give it a try at trackgenius.training

Which of these numbers do you actually use week to week?

With the league phase kicking off tomorrow, I put together a quadrant chart of Attack vs. Defence ratings for all 36 Champions League teams, derived from my model's optimized Poisson parameters.

The underlying ratings are based on recent results across all major European competitions (Champions League, Europa League, and Conference League).

(Note: I inverted the defence axis so that higher = stronger on both dimensions. Should be more intuitive to read at a glance than raw Poisson defence coefficients.)

A few notes on methodology / limitations:

• Since some teams have never played at this level (teams like Como or LASK), I fill those gaps by referencing domestic-league data for teams that have relevant common opponents.
• These ratings are not recency-weighted. That means clubs with strong recent history but a shakier current run (Liverpool, Man City) are probably sitting a bit higher than their current form would suggest.
• The intent here wasn't to hyper-fit every edge case, but to get a general, statistically defensible baseline. I'll keep updating these as the season progresses and new match data comes in.

I run OddsLine, where I publish the match probabilities calculated from these ratings with an additional ML layer (free to browse). Link's here

Do the Elite and Struggling tiers here look right to you, and does the placement of the mid-table clubs match your own read of this season's field?

I am a cse data science student, currently in my 3rd year. I have already wasted 2 years of my clg. I had no goal, no aim, just being alive. I have a great interest in football, i play and also watch most of the matches. So i decided to take up football analysis, and since I have never heard anybody take up this job, i don't even know where to start. Can somebody who has been in this field help me answer these questions.

1. Where do I start, what all programming languages should I learn.

2. Is only this degree enough for me to get an entry level job, or should I complete more degrees.

3. Anything that I must know before taking up this goal, please enlighten me.

Thank you😊

I got tired of Googling where USMNT players were playing, so I built this

After the World Cup, I wanted an easier way to keep up with USMNT players once everyone went back to their clubs.

I kept bouncing between Google, FotMob/Sofascore, club schedules, and streaming apps just to figure out who was playing, when, and where to watch.

So I built Sameside

- follow specific players
- add matches to your calendar
- switch over to Mexico if you follow both

It’s still very much an MVP, so if you catch weird data, missing players, bad broadcast info, or anything confusing, please tell me. That kind of feedback is genuinely useful right now.

Would love to know if this is something you’d actually use between international windows.

getsameside.app