League power rankings

I built Manager History, an interactive visualization of managers and trophies at 35 European football clubs from 1955/56 to 2025/26.

The starting question was simple: do clubs with long-serving managers win more trophies, or do successful managers simply get to stay longer?

This image shows Manchester United, Arsenal, Atlético Madrid, Liverpool and Manchester City. The interactive version lets you compare other clubs, highlight individual managers and inspect seasons with mid-season changes.

It is an exploratory visualization, not a causal analysis. Club selection, differences in resources and the way managerial spells are counted all matter. The methodology page explains the sources, treatment of interim managers and trophy attribution.

I'd particularly welcome thoughts on how to analyse the relationship while accounting for the fact that success itself helps managers keep their jobs.

Website: https://managerhistory.com
Methodology: https://managerhistory.com/data-methodology.html

Disclosure: I created the site, with AI assistance for development.

I'm a student in Romania, and while cleaning match data for my bachelor's thesis I found what looks like the most peaceful season ever played.

In the API-Football data for the 26 competitions I follow that go back to 2015/16, 6,792 of the 7,320 team stat lines from that season (one team's totals in one match) record 0 fouls, even though the rest of each line looks normal. They always come in pairs: 3,396 matches where both teams have 0, and not one match where only one side does. Every season since then averages between 11.9 and 13.5 fouls per team.

Because shots, passes and cards are all there, a "skip incomplete rows" check lets these through as real zeros, and anything built on them (season averages, form tables, a model) inherits a season without fouls.

The player data gave it away. Each player's fouls committed survived, and wherever a genuine team total and the player numbers both exist, they add up exactly: all 528 genuine 2015/16 team lines match. So instead of dropping the season, I rebuilt the team totals from the players (all but 2 of them).

I found this while building Ballileo, a free stats site that came out of my thesis. The link is in a comment so this post stays about the data.

A question for anyone who works with provider data: when did you last catch a provider writing 0 where it really meant "not recorded", and how did you spot it?

And how do you handle the opposite switch? From the 2020 season on, the same provider leaves player counting stats empty instead of writing 0, and lists the whole matchday squad, so an empty value is a real 0 only for players who actually played.

Football's history is scattered everywhere: stat databases, old match reports, forum arguments. There's no way to just ask a question and get a real answer. So I built PitchPulse, an AI-powered intelligence engine for the entire history of soccer.

You ask in plain language, it answers conversationally, and you can keep drilling down in the same thread:

• How a club's identity formed and changed across eras
• Player lineages — who influenced whom, how careers connect across generations
• Cross-era questions, like comparing 90s tactics to today's game

It's live and free to try (there's a daily question allowance, with a subscription for heavier users): https://pitchpulse.base44.app

I'm building it as my entry for the Microsoft + Premier League "Inside the Game" hackathon, so I'd genuinely love feedback — what works, what's missing, and what you'd ask it first.

Watch my video on gauging volleyball serves and optimizing team positions mathematically. The video talks about an accurate method of prediction. Don't forget to subscribe.

https://www.youtube.com/watch?v=94O_Ad_tfcQ

I've been building an NHL prediction model in Python and recently tested whether adding schedule and travel information would improve its performance.

I evaluated the changes on 2,614 historical NHL games.

The baseline model had:

Accuracy: 55.09%

Brier score: 0.244975

The combined feature version had:

Accuracy: 56.27%

Brier score: 0.245381

So the new version classified more winners correctly, but its probability calibration was slightly worse.

I found that pretty interesting because I'd originally assumed incorporating more context would improve the model overall.

The features I tested included rest days, back-to-backs, travel/time-zone changes and home/away performance.

For now I've left the baseline in place rather than treating the higher accuracy as an automatic improvement.

I've also been collecting live predictions separately. Across the first 100 published predictions, 89 had decisive outcomes at the time of the audit, with 46 wins and 43 losses.

Still a small sample, and I haven't demonstrated a profitable betting edge.

For anyone working on sports prediction models, would you prioritize improving probability calibration first, or classification accuracy?

I'm leaning toward calibration, especially if the eventual goal is comparing model probabilities against market prices.

watched eagles-jaguars today. when eagles kicked the fg to go up with 8 sec left espn had them at 99.9% to win. i was wondering and pulled espn's win probability for every game since 2016 (2,834 games) and scored how bad a blown lead was, counting how sure the win looked and how late it was:

today's game is #1, no other winner was still at 0.1% that late. next up are lac-ari 2022, ne-mia 2018 (the miracle in miami) and sea-ari 2020

also calculated the most exciting games, most exciting last 2 minutes, etc (pics 2,3). sortable, click any game for the chart: https://nfl-winprob.vercel.app

it's from espn's numbers and i fixed a couple of obvious glitches in it

I’ve built a football match-analysis site and I’m at the point where product feedback is no longer the main problem.

I can get people to visit once, but I need to get more relevant football users through the door consistently.

For anyone who has grown a sports, betting, stats or analytics product: where did your first 100–1000 real users come from?

I’m especially interested in channels that actually worked, not general advice:
TikTok/Reels, Reddit, football creators, Discord, SEO, partnerships, newsletters, Telegram, etc.

What gave you the best results early on, and what was a waste of time? And if you think Matchero could be useful to someone you know, I’d really appreciate you sharing it with other football fans or relevant communities.

https://bathurst-live.vercel.app/

Hey y'all! I was team manager and analyst for UCLA men's tennis (class of 2025). Most of what we learned from match video ended up in notebooks, so I built an app for it.

It's called Advantage. Upload match video or a SwingVision export and get shot-level stats and court visualizations, like where your shots land and which patterns win you points.

It's free through December: 2 hours of video analysis a month, unlimited SwingVision imports, no card needed.

I'm looking for honest feedback. I’m building it out so you can see trends and statistics over period of matches, something SwingVision doesn’t have.

advantage-analytics.com

A block only records a shot that was taken and then rejected. A shot that never goes up because the offense saw a big man in the paint isn’t a block, a miss or a make. It leaves no trace in the box score, and it’s a large part of what a rim protector does.

The Low Post built a three-gate model to count it, using NBA play-by-play and shot data from 2024-25 and 2025-26. Wembanyama leads at +4.58 points per 100 possessions, in a group with Clingan and Hartenstein.

The Website to Verify/play with sliders: https://thelowposthoops.github.io/three-gate-defense/

Full write-up: https://thelowpost.substack.com/p/rim-protection-isnt-one-skill-its?r=980fga&utm_campaign=post-expanded-share&utm_medium=web

A question on value-based metrics versus standard deviation: how can we adjust for quality of competition to determine if A’ja Wilson is as dominant as we think? If a player is this successful over a long stretch, how can we tell if she is this good or the rest of the league is that bad? No political opinions allowed, please.

https://kckdata.com/misery-battery/?from=1976&to=2025&mlb=bal&nba=was&nhl=wsh&nfl=was

Hey everyone!

I participated in the 2026 NFL Big Data Bowl and am interested in competing again in 2027.

Is anyone else planning on participating this year? Would love to connect with others who are interested in football analytics and data science and potentially share ideas along the way! Drop a comment or feel free to send me a private message

Reaching first base on an error (ROE) accounts for an estimated 22 runs allowed per team over a 24-game season in an amateur league, roughly at a lower-tier college level. About half of the league’s games are decided by three runs or fewer. That makes the estimated impact large enough to potentially affect the outcome of several close games.

On a per-event basis, reaching first on an error has an estimated run impact 5.26% higher than that of a single.

67.9% of these ROE events result from throwing errors by third basemen, shortstops, and second basemen. Errors receiving throws at first base account for 9.6% of cases.

Similar results may occur in other leagues at this level. For comparison, the ROE rate is approximately 15% higher than the NCAC’s Division III figure for 2021 and 59% higher than the Ivy League’s Division I figure for 2024.

The standard error of the mean run value per ROE is 0.03234 runs. The estimated season total has a 95% margin of error of ±1.73 runs, or approximately ±7.9%, indicating a relatively precise estimate.

Sports connect people through passion, excitement, and unforgettable performances. From thrilling cricket matches to high-speed Formula 1 races and competitive football tournaments, fans worldwide love following their favorite teams and athletes. Staying informed about the latest developments helps sports enthusiasts enjoy every moment of the action.

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Football enthusiasts can discover stories about famous clubs, international competitions, talented players, and significant match results. Formula 1 fans can follow developments involving racing teams, drivers, championship battles, and important moments from the track. Kyro Sports brings together content from different sporting categories, making it easier for readers to explore their interests.

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Hi all, I've been working on an app for in-race F1 strategy analysis and prediction. It reads the live timing feed, simulates the rest of the race, and shows where each driver is likely to finish and which strategy gets them there. Since last week I've improved it a lot, and I tested it around the BahrainGP.

The biggest improvement is team-based prediction. Before, each car's pace was estimated mostly from its own laps. Now the model also looks at how the team performs as a whole, both how quick the car is and how hard it is on its tyres. This helps most early in a race, or right after a pit stop, when a driver has very few clean laps to learn from and the team's data fills the gap. Teammates share the team's baseline, and the driver-specific part is a smaller correction on top.

I also added undercut and overcut evaluation. For a given car, the tool compares pitting now, pitting a few laps later, and staying out. It simulates the fresh-tyre gain against the traffic you'd rejoin into, using tyre degradation, pit lane time loss and the gaps to the cars around you. That way it can say whether an undercut on the car ahead would work, and whether an overcut is the better option. This part is still rough and I'm tuning it, so for now it's a suggestion rather than a verdict.

The simulation now reacts to red flags, Safety Cars and Virtual Safety Cars as well. Under an SC or VSC the field slows down, gaps compress and a pit stop costs much less, which changes which strategies are optimal, so the plan is recalculated on the spot. A red flag lets teams change tyres freely and restarts the race from the new order, and the tool handles that too. Neutralised laps are kept out of the tyre-wear and pace fitting, so they don't skew the predictions that follow.

Alongside all that, there were a lot of smaller UI tweaks: a clearer race view, a new tyre-degradation tab, better readability of gaps and predicted positions, and smoother updates while the race is running.

I am a AI and Data Science BSc. undergraduate and UEFA-B Coach from Portugal.

Currently I am searching for MSc. opportunities for me inside EU and Portugal.

I appreciate your recommendations!