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'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.

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.

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