Sports Info Solutions’ Post

As you know, the NFL Draft is coming up. Teams have started using analytics more aggressively in the evaluation process. PFF has led the movement in data analytics, which has revolutionized how some NFL teams think about certain players. Their NFL draft tools provide excellent scouting profiles, advanced statistics, and player grades. Neil Hornsby started grading NFL players in 2004 as a hobby and slowly turned it into Pro Football Focus. Get the full breakdown of PFF’s story and journey to a $160M valuation below.

The end of April saw us see one of the early marks of the 2024 NFL season with the NFL Draft from Detroit. It’s always an exciting event and this year’s event saw record attendance. Very cool for the city of Detroit. But I wanted to talk a bit more about the draft itself. Specifically, the incredibly challenging and frankly daunting process that the draft is. That is true for the fans hoping their picks turn into stars but mainly the GMs, scouts, and coaches. I’m immersed each day in the world of data and how sports can benefit from advanced metrics and data. Even with my passion for this field, I have to admit, I do not believe any amount of data or analytical approach can prepare teams truly for what the draft brings. It’s quite literally what you can call the most unpredictable thing in sports. In any of the major leagues. You have all the advanced research and data you want, but once those players get in those locker rooms and amongst their teams, everything changes. Teams look to make the most educated picks they possibly can, as they should. Still, they don’t know how things are going to pan out. It’s one of the scarier things in sports and at the same time, what you can call the most beautiful. The draft is so unique. Has us fans coming back for more and more with the hopes of the next star landing on our team. #NFL #Sports #NFLDraft

🚨 New Substack Post! 🚨 In my latest article, I dive into how polar charts can offer a quick, visual snapshot of an NHL player's performance. 📊🏒 → Using 2023/2024 season data, I break down player stats into percentiles, making it easy to compare key metrics across the league. → Whether you’re analyzing offense, defense, or overall impact, polar charts provide a clear view of a player's strengths and areas for improvement. Check it out!

I had to share this because I am so blown away by it. A truly awesome example of challenging all existing assumptions, thinking differently and using data smartly. Really envy the people of Brentford who came up with this. They got ideas from other sports like NFL which also shows the importance of having RANGE ( broad knowledge of multiple diverse areas ). It just gives you a great database of examples to seek inspiration from. It’s from football which is the easiest sport to understand. So anyone can read it and understand. If you are in the business of using data to find opportunities and competitive advantages, you should follow how they use analytics in sports. It’s my favourite place to learn how to find and use data. https://lnkd.in/dKZ9DSmp

Last week was the NFL Draft Combine and the headline of the event is the 40 yard dash, the ultimate measure of speed for NFL prospects. A record 4.21 was ran by Texas' Xavier Worthy, breaking John Ross' mark of 4.22. But despite how often 40 times get talked about, does that actually measure how well someone will perform in the NFL? This article discusses this topic and gives you an idea of how data analytics can be used in the world of sports to predict performance for prospects. https://lnkd.in/gQ_ecTDV

Weekly Data Download: 2023 Review MatchQuarters weekly look at the data from the NFL's previous week. - Coverage Usage - Blitz vs. Pressure Rates - Coverage Disguise - Offense vs. Coverage - Personnel Usage | https://lnkd.in/gNSSmpUX #ArtofX #NFL #NFLkickoff #dataviz #analytics #football #footballcoach #footballcoaching

🏈 Exciting Insights from the 2023 NFL Season! 🚀 I just wrapped up a deep dive into the offensive performance of NFL teams during the 2023 regular season, and the findings are eye-opening! 📊🔍 I visualised the Run and Pass EPA for each NFL team, creating a quadrant scatter plot that sheds light on offensive efficiency. 🏆 Key Insights: 📈 EPA Unveiled: Expected Points Added (EPA) breaks down each team's performance against expectations, offering a glimpse into their efficiency levels. 🏃♂️ Rush vs. Pass: From ground game dominance to aerial assaults, witness the balance (or imbalance) in offensive strategies across the league. 🔄 Strategic Quadrants: Teams are categorised based on their pass and rush EPA, providing a clear snapshot of their gameplay dynamics. Check out the complete Documentation and Insights: https://lnkd.in/ddQzYxWn #NFL #DataVisualization #FootballAnalytics #StrategicInsights #PythonDataViz

In the spirit of the Super Bowl, I'm reposting one of my first data visualizations I created in R. It depicts the weight distribution of NFL Offensive Linemen and how it has changed over time using a combination of box-whisker and jitter plots. I used the facet_grid function to partition by position (i.e. Center, Offensive Guard) and Decade so you're able to see how the distribution changed within a position over the decades as well as in comparison to the other positions. Analysis: You can see how much heavier the linemen are in comparison to the average Offensive Linemen in the 1970s who weighed 255 pounds which is equivalent to the weight of a tight end or defensive end in the today's game (there's only a few outlier linemen lower than 255). A small percentage of linemen were 300 pounds or greater in the 1980s where as majority weigh in excess of 300 pounds in the 2000s and 2010s. There's a noticeable median weight increase from the 1980s to 1990s and from the 1990s to the 2000s followed by a much more subtle increase from the 2000s to the 2010s (with the exception of Offensive Tackle). Could be that the weight distribution range of the linemen in the 2000s and 2010s optimal for the current offensive schemes (which tends to lean more pass oriented) of NFL and any heavier would be a detriment to the scheme (and player) rather than an asset. The distribution range in the 1990s was much more variable than any other Decade for all positions but then contracted in the 2000s and 2010s as an optimal weight distribution range (fair amount of players weighed 280 pounds or less in the 1980s and 1990s, only a few outliers were less than 280 in the 2000s and 2010s) seems to have been found. Various factors have contributed to the size increase such as the professionalism and profitability of the game (modern players generally don't have to work a full time job in the off season and are able to focus on football and football related training almost exclusively, https://lnkd.in/gJTQFiVY) has changed and improved including how the game is played offensively. Understanding of training and nutrition has evolved and improved as well. I can't imagine these players will get any bigger but I'd be curious to see what the distribution of the 2020s are...stay tuned??? 🤔 #superbowl #analytics #datavisualization #nfl

🚨 New Substack Post on My Master's Final Project in Football Analytics 🚨 I’m excited to share my latest project, where I dive deep into football player role classification and evolution tracking using data analytics! 🧠⚽ In this article, I explore how data can uncover player roles beyond the traditional labels of "defender," "midfielder," or "forward." By analyzing performance data from Europe’s top leagues, I built a model that clusters players based on their contributions on the pitch and tracks how their roles evolve over time. Curious to learn more? 📊 https://lnkd.in/eagCUbyu #FootballAnalytics #DataScience #SportsAnalytics #Football #MastersProject #BigData #PlayerRoles

Moreyball (sic.): How the Houston Rockets' broke records 2x in a row using analytics. I'm more of a Football fan but this one from basketball is fascinating. There are some inspiring stories of teams bring in big results with some carefully crafted strategies, and Daryl Morey's journey with NBA's Houston Rockets is a slam dunk! 💯 Former consultant and MIT Sloan grad, Morey, took the basketball world by storm with his data-driven approach. He asked: "What if we could optimize our shots to win more games?" Morey analysed that three-point shots were way more efficient than two-point shots (except those flashy dunks)! Corner threes, in particular, have a higher success rate. And if it had to be two pointers, then it was in the restricted area near the basket. Wicked genius. The Rockets' new strategy: Design plays to get top shooters open for corner threes. Ditch those mediocre two-point shots completely. Focus on high-percentage shots. The results were 💥 Broke the record for most three-point shots made in a season twice! Led the NBA in wins and points per game! Now, other teams are following suit. Moreyball for you :- - Identify industry inefficiencies. - Use data to drive decisions. - Stay adaptable. Thoughts? How do you use data to drive innovation in your field? #SportsAnalytics #Innovation #DataDriven #HoustonRockets #NBA