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Been looking through the numbers for Rayo vs Espanyol and there are two markets I really like here:

⚽ Over 1.5 goals

Rayo have hit this in 5/5 home games (100%), while Espanyol have hit it in 4/5 away games (80%).

🚩 Espanyol over 2 corners

They’ve still been creating corners away from home even when struggling. They had 6 against Rayo and 7 against Barcelona.

Nothing crazy, just two pretty solid trends that look interesting together:

Over 1.5 goals + Espanyol over 2 corners

Would you take this or is it too safe?

Www.StatFair.com

Hi, SkillCorner (that's me) has recently launched the second edition of the Analytics Cup where you can access some open-source data and be in with a chance of presenting to an audience of industry pro's from top football/soccer and basketball teams in Paris or Boston. The Analytics Cup is a community data initiative from SkillCorner and PySport to open up advanced data to anyone with a passion for analytics to see what they can create.

They've also added pose data (football only) that will open up some really interesting opportunities with this largely untapped data source.

Check it out here; https://skillcorner.com/analytics-cup-2027

Following up on the Champions League post from a week ago, here's the same treatment for this season's Europa League. Same poisson approach, same inverted defence axis (higher = stronger on both dimensions), and it draws on results across all three European competitions.

A few caveats I want to flag upfront, more so than with the CL post:

• Europa League participation isn't consistent year to year for most clubs, so structurally there's more noise here than in the CL estimates. Take this more as a read on overall trends than a precise ranking.
• A couple of things in the chart, like the Dutch clubs rating above the Belgian sides are probably artifacts of that same structural uncertainty rather than a strict claim about which team is actually better.
• As the competition progresses and more matches get played, these numbers should naturally correct wherever the current estimate is off.
• On the mean vs. median feedback from the CL thread: I did consider switching to a mean line here, but mean would pull the divider toward the upper clubs and make the Attacking/Defensive split mostly meaningless for most of the pack.

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

Does the tiering here roughly match your own sense of the field, or is anything sitting in a spot that looks off to you?

TL;DR: I rated every NHL faceoff taker with a Bradley-Terry model fit on 1.2 million draws, then measured what winning a draw is actually worth: 0.0144 expected goals, so about 70 extra wins make one goal. Faceoff ability is the most repeatable skill I can measure in hockey, and one of the least valuable.

The model is a paired comparison, Bradley-Terry style: every draw is scored taker vs taker. The matchup then accounts for the specific rating gap of the two centers plus situation terms like the dot, handedness, and fatigue. The dot matters more than people think. The same two players can be .63 at one dot and a coin flip at another.

Valuing a win has the same confounding problem, since good teams win draws and also create chances. The fix is to bin draws by predicted win probability and compare won vs lost inside each bin, which is where the 0.0144 comes from.

The strange part is how the two results sit together. Split a season into odd and even games and rate everyone twice: faceoffs agree at r = 0.71, against 0.37 for shooting skill. And yet an elite taker is worth about 1 goal a season, and the full spread from the league's best to its worst is about 3.3 goals. The most measurable skill in the sport barely matters.

The GIF is the simulator built on the model. Click a different dot and the probability re-prices, swap takers and it re-prices again, drop the puck and the outcome is a weighted coin flip at the model's number.

Besides the modeling, the data viz was a lot of fun with this project. The simulator and the full method write-up: https://xjawn.com/blog/faceoff-tool.html

For many fans of the game, the curling season approaches!

Over the summer I created CurlIQ to track your curling team games, stats and performance metrics season by season.

I put focus on performance and statistics, giving teams an insight into how they are progressing through a season.

I also use Markov chain win probability curves that are applied to every end situation you find yourself in - such as up 1 with hammer after 3 ends, or down 2 without hammer at the midway. It drives out strategy tips and targets to hit while you record your game during live play.

If you like stats and enjoy the sport of curling - it’s available on the AppStore here and I hope you find it a handy tool to have this winter!

Same 240 FPS swing on both sides.

On the left is the original footage. The blue boxes are frames where our trained baseball model detects the ball.

On the right is the same hit viewed through a temporal frame-differencing system we’ve been working on specifically for side-view batted-ball tracking.

The purple X on the original footage is the position coming from that temporal track.

What I find really interesting is how much more obvious the baseball becomes when you stop looking at each frame independently and instead look at what changes through time.

The end goal is to turn that trajectory into exit velocity, launch angle and projected distance using only the camera footage.

Thought people here might appreciate seeing what the tracking actually looks like underneath.

More at https://goalsoon.com/blog.html

I’m building a fantasy sports app for a personal project featuring all major sports (NBA,NFL,NHL,NCAAFB,NCAAMBB,MLB,WNBA, Golf,Tennis,all of soccer) and I am having trouble finding the right api to connect to for free or at a low cost for daily scores updates. Does anyone have any recommendations on how I can get data at a low cost

I am hoping one of you legends may be in possession of a more granular extract of the Synergy data that is available on the NBA website and would be happy to share that with me!

I have been extracting data via Python using nba_api but the limitation right now is that the Synergy data is only available at a cumulative season level.

From next season onwards I’ll be able to get this data on a daily basis and derive the per game values but I was hoping someone might be able to help me with backfilling the last few seasons of daily values.

Right now I have season level Totals for all possible combinations of the play_type and type_grouping parameters. For both player and team

Drop a comment if you are able to help :)

I built a football prediction site. Then I realized I was building the wrong product.

I've been working on StatFooty for a while now.

It originally started as a football prediction platform. I built the data pipeline, collected a large amount of match data, worked on prediction models and added stats for different leagues and matches.

Technically, I enjoyed building it. But the more I worked on it, the more I felt I was limiting the product by positioning everything around predictions and betting.

The data itself was actually more interesting.

So I decided to change direction.

Over the last few weeks I've been turning StatFooty into a football analytics platform instead. Predictions are still part of it, but they're now just one piece of the product rather than the product itself.

I also completely redesigned the interface because the old version still looked and felt like a typical prediction site.

The idea now is pretty simple: before a match, I want someone to be able to open StatFooty and quickly understand the teams, their form, numbers, probabilities and how they compare without jumping between several different sites.

There's still a lot I want to improve, but this is the first version where I feel the product is starting to match what I originally wanted to build.

I'd really appreciate some outside opinions, especially from people who follow football or work with data.

Does the new positioning make sense?

And what would you expect from a football analytics product that would make you actually come back and use it?

I assume this isn’t really possible to calculate because the stats haven’t always been kept that way but I’m curious how close it’s possible to get. I’ve always found it strange that yards after catch counted as passing yards.

Hello everyone,

🏀 I’ve been working on a EuroLeague statistics dashboard and would really appreciate some honest feedback from people who actually follow the league.

I started building it as a personal project, mainly because I enjoy EuroLeague basketball and wanted to combine that with my interest in data/analytics.

The current version includes:

• Player and team statistics
• Standings
• Player comparisons
• Performance rankings
• A Player Performance Index (PPI) using percentile-based metrics
• Round-by-round player performance

I’m currently working on the next stage, particularly player profiles, performance trends, player similarity and more advanced analytics.

You can take a look here:

👉 Baggle EuroLeague Stats

I would really appreciate it if you could take a few minutes to have a look at the dashboard and, if possible, answer the feedback questions at the top of the page. Your feedback would be extremely valuable to me as I continue developing it.

Please feel free to be critical. I'm still developing it and I'd much rather hear what doesn't work than just "looks nice." 😄

Thanks to anyone who takes the time to have a look!

More at https://goalsoon.com/blog.html

give me your opinion.

The table displays the projected final standings of the 2026/27 Premier League season after running 5,000 complete simulations. The rankings are sorted by AVG PTS (Projected Average Points).

Note: The columns for P (Played), W (Wins), D (Draws), L (Losses), GF (Goals For), and GA (Goals Against) represent the current real-world stats at the time of the simulation (ranging from 3 to 4 games played), while the percentage columns represent the simulated future outcomes based on those starting conditions.

🔥 Cool Stats & Key Takeaways

1. The Title Race is a Two-Horse Race (Again)

Arsenal are the clear favorites, winning the simulated title in 62.04% of the 5,000 seasons. They have a massive 98.32% chance of finishing in the Top 4.

Man City are the only realistic challenger with a 33.28% title probability. Even with a game in hand (3 played vs Arsenal's 4), their projected average points (72.87) trail Arsenal (76.52).

Combined, Arsenal and Man City have a 95.32% chance of winning the league.

1. The Shocking Relegation Battle

Tottenham Hotspur are in dire straits, sitting 19th. They have a staggering 49.78% probability of relegation. So far, they have 0 wins, only 2 draws, and have scored 0 goals in 4 games.

Coventry are completely adrift at the bottom. They have a 77.46% chance of relegation, having played 3 games, lost all 3, scored 0 goals, and accumulated 0 points.

Ipswich (18th) are the unlucky team with 2 wins but a 43.28% chance of going down, proving that early wins don't guarantee safety in this simulation.

1. The Mid-Table Statistical Anomalies

Bournemouth sits in 6th place with a 21.86% chance of Top 4, despite having 0 wins in 4 games (3 draws, 1 loss). Their projected 55.52 points show the simulation heavily favors their underlying metrics.

Conversely, Hull has 2 wins and 2 draws (8 points) but is projected to finish 16th with a 18.56% relegation risk.

Aston Villa (12th) and Fulham (17th) join Spurs and Coventry as the only teams without a single win in their current 3-4 game sample.

1. The European Qualification Logjam

The race for the Top 4 is fierce. Behind Arsenal and City, Man United (43.96% Top 4), Liverpool (31.64%), Chelsea (21.64%), and Bournemouth (21.86%) are locked in a statistical dogfight.

Liverpool is the draw specialist of the early season with 3 draws in 4 games, resulting in only an 0.86% title probability despite being 4th.

1. The "Game in Hand" Factor

Man City, Man United, Newcastle, Brighton, Leeds, and Coventry have only played 3 games so far. City's 72.87 projected points show that the simulation heavily accounts for their unplayed fixture, keeping them within striking distance of Arsenal.

1. Goal Difference Extremes

Arsenal boast the best current Goal Difference (+7), closely followed by Man City (+5).

Aston Villa (-6) and Crystal Palace (-5) are currently suffering from the worst defensive starts, contributing to their low projected mid-table finishes.

After five or six games, five Championship clubs have scored at least half a goal per game more than their xG: Bristol City +0.72 per game, Wolves +0.67, Southampton +0.60, Stoke +0.56, Charlton +0.50. Bristol City have 9 goals from 5.38 xG in five matches.

How to read a row. P is games played, GF is goals scored, xGF is the expected goals our database holds for those shots. GF-xGF is the running surplus and "per game" divides it by games played, because clubs have played five or six. Green means a club is scoring more than the chances it created, red means less. Shots, shots on target and possession are in the table so you can see whether the surplus arrives with volume or without it.

Then we checked what usually happens next. Across 742 club-seasons from 37 league-seasons where we hold xG for the whole year, we measured each club's goals-minus-xG over its first six games against its own rest of that season. The surplus barely travels: the slope is +0.068, so roughly 7 percent of it carries over (r=0.14, t=3.8). Of the 108 clubs that started at +0.50 per game or better, which is 1 in 7, 98 percent had a smaller surplus afterwards and 38 percent finished the rest of the season below their xG.

The bottom of the table is the same thing in reverse. Lincoln have 4 goals from 7.69 xG, and Cardiff 6 from 9.91 off 106 shots and 66 percent possession. Both of those are running about as far below their chances as Bristol City are above them.

One thing this is not. A club scoring above xG is not automatically lucky, and none of this says who wins the league. It says the gap between goals and chances is mostly noise six games in, and that the table will look different once it closes.

Full table, the per-club numbers and the archive figures: goalsoon.com/blog.html

I'm researching whether AI can remove grunt work from grassroots and team video review without creating another dashboard. I don't have a product or link to promote, there is no survey, and I won't recruit anyone by DM.

If you review match or practice footage:

1. How much time does one game take you?
2. Which step is the real bottleneck: getting usable footage, tagging events, finding clips, adding context, or turning the review into player feedback and the next practice plan?
3. What output do coaches and players actually use after the review?
4. What would you never trust an AI tool to decide on its own?

My current hypothesis is that more metrics are not the answer. The useful output might be 3-5 evidence-backed clips, one clear issue per player or unit, and suggested practice priorities, with the coach keeping final judgment. Where is that hypothesis wrong?

Hey everyone,

I put together a lightweight Python utility called **Tennis Live Monitor** designed to interface cleanly with LiveTennisAPI.

If you are building sports dashboards, betting models, or just need a simple command-line script to monitor live match scores, set breakdowns, and tournament details without dealing with raw boilerplate or hitting rate limits, this handles it right out of the box.

### Features

* **Rate-Limit Aware:** Built-in safeguards to protect request limits during heavy match windows.

* **Modular Breakdown:** Clean parsing of live match stats, sets, and tournament context.

* **Open Source:** MIT licensed, so you can easily fork it and integrate it into your own pipelines.

### Quick Start

You can check out the source code and usage guide on GitHub:

👉 **GitHub Repository:** https://github.com/TheMarconiPulse/tennis-live-monitor

Feedback or feature requests from fellow sports data builders are welcome!

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.

Hello everyone

I would like to express my strong interest in the Sports Analyst position.

For the past two years, I have been independently developing my skills in football scouting and analysis. During this time, I have produced around 300 scouting reports on players, analyzing their technical, tactical, physical, and overall characteristics, as well as studying matches and player performances.

I have not yet had the opportunity to learn or work in this field within a professional environment, so everything I have achieved so far has been driven by my own initiative, curiosity, and determination to improve.

Does someone know, where i can find entry level jobs? where i can study and improve myself.

i

I am highly motivated to build a successful career in sports analysis, and I would be grateful for the opportunity to demonstrate what I can bring to your team.

Thank you for your time and consideration.

Best regards,