I've been exploring the ebbs and flows of parity within various sports leagues primarily to justify betting heavy favorites. If a top 5% team plays a bottom 5% team what can we expect to happen? I would like to hear any ideas on what metrics would be used to measure and quantify the overall level of parity/ "competition abundance" within any given sports leagues. I'm pretty open to any ideas so any suggestions, references, or resources for ideas are appreciated.

I built a pipeline that turns FIFA's official Post-Match Summary Report (PDF) into a single radial visualization of the entire match. Every shot, possession spell, save, card, sub, and momentum swing across all 120 minutes.

How it works:

1. Parse the FIFA PMSR PDF into a structured JSON schema (events with minute, team, type, and outcome)
   
2. Add per-minute momentum from BBC Sport's live coverage
   
3. Render programmatically as SVG, export to PNG

Encoding: time runs clockwise from kickoff (0') to end of extra time (120'). The outer bloom is momentum (which team was more likely to score, per minute — Spain threatened in 108 of 135 min vs Argentina's 27). Rings moving inward: shots (dot per shot at its minute, filled = on target), cards/subs, goalkeeper actions (Martínez made 11 saves). The inner dot cloud is possession sequences.

Some things the format surfaces well: Spain's xG dominance (2.52 vs 0.07) reads as visual density, Argentina's shift to a low block (33% of their defensive time) shows up in the shape data, and the Fernández red card at 90+3' visibly precedes the extra-time momentum collapse.

The pipeline is repeatable for any match with a FIFA report, so I'm planning to run it across the knockout rounds.

Happy to go deeper on any of it!

I’m researching how football coaches and analysts review matches after they’ve been recorded.

I’m particularly interested in understanding:

• What your analysis process looks like
• Which tools you use, whether that’s Excel, Hudl, Veo, or something else
• Which parts take the most time
• How the analysis influences training, tactics, or player feedback

I’ve created a short survey based on real, recent match-analysis experiences. It should take around three minutes:

https://tally.so/r/lbZLeW

At the end, you can also apply to have one of your matches analyzed for free. I’m looking for honest experiences even if you rarely analyze matches or find the existing process good enough.

Been working on MLS analytics as part of a broader football modelling project, and one thing I kept running into was the complete absence of a clean, consistent Elo dataset for MLS. Unlike most major European leagues where historical Elo data is reasonably accessible, for MLS it simply doesn't seem to exist in any usable form online.

So I built one.

The chart above shows Elo rating trajectories for every MLS club across their full history. A few things jump out immediately: Inter Miami's sharp upward curve after the Messi arrival, the long-term decline of founding clubs like DC United and Sporting KC, and the rapid ascent of newer sides like LAFC and Vancouver Whitecaps.

The dataset covers every MLS club from the league's founding to the present, is updated on an ongoing basis as new results come in, and is free to use with attribution.

GitHub: https://github.com/philo92/mls-elo

Happy to discuss methodology, parameter choices, or anything else in the comments. And if anyone has thoughts on how to extend or improve it, I'm open to that too.

Does anyone have a dataset or data website for multi-object tracking and event recognition in football? I’m aware of SoccerNet and SoccerTrack V2, but I’ve found that the SoccerTrack V2 data doesn’t provide the source data for MOT.https://atomscott.github.io/SoccerTrack-v2/

I wondering is there any structure lessons on how to become football analyst.

What kind of things to watch in the game when scouting the teams and what when scouting individuals?

An article I wrote for Sports Info Solutions using k-means clustering to build pass rusher archetypes. The summary findings are as follows:

• For nose tackles and defensive tackles, the main clusters are broken up into power rushers vs. non-power rushers, with some additional alignment separators too.
• There aren’t many defensive ends who spend their time aligned in the 4,5, or 7 technique on a consistent basis. The ones that do might have their preferred move, but there isn’t a large enough sample size to define real clusters from them.
• Edge rushers are broken out by stance type at the general level, but then break out more at the sub-level. There are 8 sub-clusters for the 2-point stance group that all feature a unique move. There are 2 sub-clusters for the 3-point stance group that break down the power rushers vs. players who are more balanced.
• The speed and spinners from a 2-point stance have the highest pressure rate, while strict power rushers from either stance have the lowest.

Enjoy the article!

A few weeks ago I shared the VAEP model that I built to try to figure out who actually deserves the credit on NHL goals. I've now completed the first version of another model that looks at the same thing - Tracked Credit.

The Tracked Credit model reconstructs the pass/carry chain for every NHL goal over the past 3 seasons (that's all we have data for) and splits each goal into exactly 1.0 "goal credits". The credits are then divided between the scorer (finish share) and everyone who contributed to the buildup (creation share). The creation share includes bonuses for hard passes, screens, takeaways, and rebounds to help make sure buildup actions are appropriately credited.

One of the more interesting things that fell out of the model is that about 12% of all goal credit (roughly 1000 goals' worth) is assigned to players who didn't get a point on the goal. Along those same lines, 2/3 goals have a credited contributor who did not receive a box score point.

There are some issues with the model that I hope to address in future versions:

• Defensemen are chronically under-credited. One of the big reasons for this is that the goal clips are short, so the breakout + transition value that they provide is missed.
• Some actions are miscategorized because I only have puck location and player location data. The model then infers who has the puck based on proximity, but it isn't perfect.
• The current model only tracks one possession chain, so if the team loses and then recovers the puck no credit is assigned to the earlier possession.

There are more details in my post, along with the option of downloading some sample data. Here's a link to the post - https://open.substack.com/pub/ocelotanalytics/p/breaking-down-who-deserves-credit-75e

Data: Simulated. I will try to plug in real data into this framework if can get my hands on it.

Tools: Python, Manim, Numpy, three.js

There is also an explainer 'How it works" page.

Excited to share my latest Power BI project — a FIFA World Cup 2026 Tournament Analytics Dashboard.

I designed this dashboard to analyze the tournament from multiple perspectives:

📌 Page 1 — Matches Overview A complete view of tournament-level match data, including: • 104 Matches • 328 Total Goals • 3.15 Average Goals per Match • Attendance Analysis • Goals by Tournament Round • Home vs Away Performance • Possession, Saves, Fouls, Crosses & Offsides • Interactive filters for deeper exploration

🏆 Page 2 — Top 3 Performers A focused comparison of the top-performing teams across key metrics such as: • Goals Scored • Defensive Performance • Possession • Shots on Target • Fouls • Cards

🥇 Page 3 — Executive Summary A high-level summary of the tournament, highlighting: • Champion • Runner-up • Third Place • Best Attack • Best Defense • Fair Play Team • Highest Attendance Match • Biggest Win • Highest-Scoring Match

The goal was not just to visualize football statistics, but to structure the dashboard as a complete analytical story — moving from full tournament data → top performers → key tournament outcomes.

🛠️ Tools & Skills: Power BI | DAX | Power Query | Data Modeling | Data Visualization | Data Analysis

This project helped me further improve my skills in transforming raw data into an interactive dashboard that communicates insights clearly and effectively.

I’d appreciate your feedback: What would you improve or add to this dashboard?

I'm creating a database that would track logged-in users "takes" for future performances for football players, and overtime we will rank the users not only for their correctness, but also their methodology for finding their results. The purpose for this is to find the most consistent models that the community uses (metrics, film study habits, coachspeak, etc.)

I'm additionally implementing methods on how to teach users how to play fantasy football through the "Film Room." The first "chapter" is built to teach a user how to formulate a take, and that is a bit experimental for me.

I must clarify, the website is constructed with the use of AI. I understand and anticipate AI slander, however the sole purpose of this post is to evaluate the overall, foundational value that it provides.

Thank you for taking the time of your day to visit my website: [https://www.wkbu.app/\](https://www.wkbu.app/)

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Hey everyone! I hope you are all doing well.

I am in the middle of my dissertation and would love your help. My research is all about women's football in the UK and I have put together a short survey that takes about 5 to 6 minutes to complete.

You do not need to be a huge fan to take part. If you have even a passing interest in women's football, your opinion counts and I would really love to hear it.

https://qualtricsxmf57p2gmq2.qualtrics.com/jfe/form/SV_em0ksX55mBUEXgW

Sharing this with anyone else who might be interested would also be a huge help. Thank you.

Does anyone have a dataset or data website for multi-object tracking and event recognition in football? I’m aware of SoccerNet and SoccerTrack V2, but I’ve found that the SoccerTrack V2 data doesn’t provide the source data for MOT.https://atomscott.github.io/SoccerTrack-v2/

As the title states, I’m looking to create this survey, and I’m looking for further input. I’m a big fan of PL fan debates and discussions, ultimate 11s, best players of all time, etc, and am doing this out of pure enjoyment and love of the game.

For context, I’m working on a Google form that I’d ideally post here later on. I’d like to get ideally over 100 submissions before posting the results in order to get the best sample size possible.

So far on the survey I have questions revolving around best players in every position, both current and all PL era, best managers, best games of all time, best club kits, best stadiums/atmosphere, and would like your input on more topics that you would like to see covered.

All just for shits and my own (hopefully as well as the community’s) enjoyment. If something like this already exists, please let me know! Any other constructive advice or input is welcome as well.

Thank in advance!

Hi everyone,

I've been helping with a website called Statistips.com and we'd really appreciate some honest feedback.

The idea is to provide probability-based predictions for sports using machine learning models built in Python. The models are trained on historical data to generate win probabilities and other statistical insights. The predictions aren't generated using generative AI—the focus is on statistical modelling and machine learning.

At the moment, you'll need to create a free account to access the dashboard.

We're really just looking for any feedback you have, whether it's about the design, usability, navigation, mobile experience, speed, clarity of the statistics, or your overall first impression. If something feels confusing, unnecessary, or could be improved, we'd love to hear it.

Thanks in advance to anyone who takes the time to check it out-- don't hold back!

Hey everyone,

I’ve been working on PlayCaller. It's an AI-driven sports intelligence platform and Data-as-a-Service API.

I got frustrated with standard fantasy sports advice: delayed injury news, qualitative "expert consensus" that doesn't actually give you a statistical edge. So I built a data engine underneath instead, real-time signal ingestion, validated ML confidence scores, matchup heatmaps.

A few specifics on the stack: continuous ingestion of live player/team/situational signals; scoring models validated against out-of-sample hit rate (not just fit to history) that output a confidence distribution rather than a single point estimate; and a clean DaaS API layer plus an MCP endpoint so agents and dev tools can query the engine directly instead of scraping a UI.

Biggest challenge was latency during live game windows, I had to be disciplined about the service layer so the API stays responsive under concurrent feeds instead of choking.

The consumer app is at playcallerapp.com; the DaaS/API product lives at api.playcallerapp.com. Two things I'd genuinely value feedback on:

1. For fantasy players: does the UI actually turn raw data into a fast decision, or does it still feel like homework?

2. For devs/data folks: what would make an API like this worth building scripts against? What's missing?

Thanks for reading.

I’m developing a football management game focused on youth academies. I’ve added VAR to the game’s equivalent of the UEFA Youth League, but I’m struggling to find reliable data for realistic probabilities.

Does anyone know of statistics covering:

• How often an awarded penalty leads to a VAR check/review?
• How often the penalty is confirmed or overturned? The probabilities for this?
• How often VAR awards a penalty after the referee initially plays on?

I know all penalty decisions are checked in the background. I’m specifically interested in checks that become visible during the match.

Any reliable report, study or league dataset would be greatly appreciated.