This article explains my Expected Score (xS) model for Australian Rules Football. Using data from 2021-2024, I share my steps, results, and top 10 lists.… Read More Creating an xScore Model for Australian Rules Football
Diving into the past decade of data across 10 different leagues to see how elite European football tactics have evolved… Read More Key Playstyle Changes Over the Last Decade: Pressing, Defensive Height, Goalkeeper Usage
Which teams may use their keeper the most in buildup? I create a data model to find, from 3,000 teams around the world, which ones might use their keeper heavily (and high up) in buildup.… Read More Goalkeeper Chains Around The World: Creating a GK Buildup Index
Pass flow motifs are a good way to begin looking into a team’s passing style and perhaps how they may play in possession. This article looks at 18 leagues and 305 teams’ unique passing styles.… Read More Pass Flow Motifs: Examining Team Passing Style
Can we determine what leagues are most similar to each other? Clubs similar to a specific club? Possible leagues and clubs for a specific team to scout in? I’ve developed a method for this and introduce it here.… Read More League and Club Similarity: Proof-of-Concept and Applications for Recruitment
“How many points should a football team have been expected to earn?” That’s the question this article develops a method for. The Pythagorean Expectation method developed in Baseball is brought into Football, with all aspects of the method and sample analyses detailed.… Read More Creating an Expected Points Model Inspired by Pythagorean Expectation
The data-vs-eyes debate in football is not a dichotomy but a spectrum. Without any videos, you miss out on precious information. Without any data, you miss out on precious information. If you really want to answer (or begin to answer) a specific question, you’ll need to ensure you place yourself as close to the optimal location on the spectrum as possible… Read More The Data-“Eye-Test” Spectrum in Football Analysis
Italy’s 3rd division is rife with young talent across the 60 teams in the league. Which stand out the most based on specific data criteria? I build a U24 “dream team” with 36 players across all 11 positions to find out.… Read More Data Scouting Serie C for U24 Players: All Positions
This article uses data to find several young, interesting LaLiga2 CMs. Many data visuals follow so we get a sense of each player’s profile and performance.… Read More Using Data to Find Interesting Young Midfielders: 22-23 La Liga SmartBank
This is a first step towards trying to get a better view of defenders and defensive skills using event data. I examine ball receipts under pressure and subsequent actions to see if attackinng-player-focused event data can be used as a proxy for defensive actions… Read More Moving Towards Better Data Analysis of Defensive Play: One Proof of Concept – Pressures
… and why they’re all misleading and/or faulty.
We’re currently in a situation where widely-available data and metrics used to analyze defenders can often be antithetical to defending itself. There’s a disconnect between on-ball events and quality defensive skills in current data accessible to the public.… Read More We Need to Talk About Defensive Metrics…
Raimonds Krollis is the best striker in Latvia. This articles analyzes him and illustrates his main strengths & tendencies including movement, dropping deep, and finishing, while discussing his areas to improve and his potential ability.… Read More Raimonds Krollis, Valmiera’s Talisman Captain: Player Profile & Analytic Scouting
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