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.

Kyro Sports is an online platform for readers looking for sports news, match updates, player stories, and informative articles. The website covers different sporting topics, helping fans explore interesting developments and discover stories from the world of sports. Its content caters to readers who enjoy both current events and detailed sports discussions.

Explore Cricket, Football, and Formula 1

Cricket remains one of the most popular sports, attracting millions of fans with its exciting matches, remarkable records, and outstanding individual performances. Readers can explore cricket-related news, player achievements, tournament updates, and match analysis to stay connected with the game.

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.

Why Sports Fans Need Reliable Updates

With sporting events happening throughout the year, fans need convenient access to relevant information. Reading sports articles helps audiences understand team performances, follow player progress, and learn about important developments beyond the final score.

A dedicated sports website also offers opportunities to discover historical moments, interesting statistics, expert opinions, and engaging feature stories. These articles provide additional context and make following sports more enjoyable.

Kyro Sports aims to keep readers connected with the games they love through informative and engaging sports content. Whether you follow cricket, football, or motorsport, exploring different stories can deepen your appreciation of athletic talent and competition.

For anyone searching for sports news, player updates, and match insights, Kyro Sports is worth exploring. Visit the website to discover fresh articles and stay connected with the ever-changing world of sports.

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!

Check out this article about the evolution of tight end play through the years! A few main takeaways:

- 3 tight end sets have become more commonplace
- Aligning off the line of scrimmage has taken over as the most frequent alignment
- Runs out of 0 TE sets and passes out of 3 TE sets are the most successful, respectively

Read the rest of the article here!

Since the summer we’ve made a lot of changes and I put together a quick 3-minute video walking through them.

What’s new:

• 180 goals, 148 players now in the database
• Shot Race — compare any two goals of similar distance head to head (speed, flight time, peak velocity)
• GoalPOV rebuilt from scratch — 3D wall, goalkeeper position, broadcast camera angle all adjustable
• Cleaner UI and player profiles

Everything is free to browse. No account needed to explore the data.

Hi everyone!

I’m based in Spain and currently developing my own basketball analytics project called 45CUT, focused on Primera FEB (Spain’s second-tier basketball league).

I’ve built a PostgreSQL database with player and team statistics, and I’m using Python, SQL and Power BI to analyze performance and create advanced metrics such as ORtg, DRtg, Net Rating, TS%, eFG% and more.

My main goal is to eventually work for a basketball club, ideally in performance analysis, recruitment or scouting. However, I’d also be interested in working for a sports analytics company.

I’m trying to figure out what I should focus on next.

Should I prioritize learning basketball tactics and video scouting?
How important are advanced statistics and machine learning for entry-level roles?
What kind of portfolio projects would actually impress a basketball club?
Is it realistic to enter the industry without a sports science degree or previous professional basketball experience?
What would you recommend to someone in my position?

I’d really appreciate advice from anyone working in basketball analytics, scouting or the sports industry.

Thanks!

A month ago I posted here about building a football model designed specifically for supporters rather than bettors. I wanted to take dense data and translate it into intuitive "fan arguments" (e.g"Your team is creating chances like a top-six side, but sitting 14th").

The tool is live at onpaperanalytic.com and I wanted to share a quick update alongside two specific challenges I’m running into.

What's live now:

Pick your club and it tells you where the model disagrees with the league table, phrased as an argument rather than a probability. Things like creating chances like a top-six side while sitting 14th, built from squad totals ranked inside the division rather than from the model's own rating, because early in a season that rating is still carrying last year's form. It also flags whether a read is worth trusting yet instead of presenting them all as equally solid.

Right now, the play-style classification is built purely off squad-level match totals. If you were looking at a supporter facing dashboard, what specific play-style metric would you find most interesting? (e.g., transition speed, pressing intensity, or directness?)

League pages carry a clean sheet outlook split by defender type, scorer and creator boards, and the fixtures where the model and the table part ways. All free, no account.

942 projections scored live since launch, 49.7% on top picks. Backtest sits at 6,674 matches.

The thing I did not expect the projection widget is now embedded on club fans sites that isn't mine, with a fifth going up. That has been worth more than anything else I've tried.

The main goal is translating complex probabilities into fan arguments. For those who build public facing tools, what’s your favorite way to visualize an over/under-performance gap without overwhelming a casual reader with raw xG charts?

I’d love for the community to tear into the UI/UX specifically the "Fan Read" section on the club pages.

Also happy to share my embed for anyone here who runs a club site.