Founder SportAnalytics.com. Former @Brewers and @SABR. @ASU @WPCareySchool Graduate with a BS in Data Analytics. Dad to the coolest 6 year old boy named Dawson.

Joined April 2023
54 Photos and videos
Dave Yount retweeted
Never been a better time for our site, show, etc. Thankful for all the hard work from the whole team and all the support from you guys.
It was a MASSIVE week for Reception Perception! 🏈 -NEW Player Comparison Tool -15 Year 2 WR profiles from @MattHarmon_BYB -RB prospect profiles from @JamesDKoh -QB prospect profiles from @AlfredoABrown
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Me begging the Vikings to fire Rick Spielman's old scouting staff.
“Gray’s on the doorstep, having interviewed for the Raiders, Chargers, Jaguars and Titans jobs over the last two cycles. He made it to the final round in Tennessee last year. Gray rose on the college scouting side in Minnesota, working under Rick Spielman and Broncos GM George Paton there, before Brandon Beane brought him to Buffalo to be college scouting director in 2017” - @AlbertBreer on Terrance Gray earlier this year
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Vikings ownership perfectly reflects the Vikings fan base. Most of the fans are perfectly content with a team that is slightly above average. Even if they never win a Super Bowl. They are so scared of becoming the Cleveland Browns that they will stay in football purgatory forever
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Dave Yount retweeted
On consensus and the power of the wisdom of the crowd: Consenus steals and players that are within "agree" band tend to hit at a higher rate than "reaches" through the first two rounds or so.
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Dave Yount retweeted
My contribution to the consensus board discussion: it’s data. Data is good. Data is not the enemy of film, experience or expertise. Data allows you to analyze your process to check yourself and stay ahead. It’s important to know how to parse it without completely dismissing it
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All this talk about good player / bad player regarding Caleb Banks. He could be the next Chris Jones and it's still a bad pick for the Vikings at #18. This is why. They could have done the same thing the Bills did and still drafted the same player tomorrow (for less money).
Gaining 22 spots on day Two for 2 spots on day One. Not bad for the Bills.
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Hey Phil, Judd, Declan, and Thor — huge fan of Purple Daily. As someone who’s worked in analytics in pro sports, I wanted to propose a new angle for discussion. First, I want to preface my research by saying that I bought a JJ McCarthy jersey after the draft. I really wanted him to be the guy. I am also a big fan of Thor's draft evaluations. So please don't take this as hate. Just wanted to add some data and talking points to your recent discussions on the matter. I built a table of every QB5 since 1980, and if you define a franchise QB as 3 Pro Bowls, the data is pretty clear. Teams are 183% more likely to hit on a franchise QB with QB1 compared to QB2. Teams are 240% more likely to hit on a franchise QB with QB1 compared to QB3. Teams are 467% more likely to hit on a franchise QB with QB1 compared to QB4. The drop-off is incredibly steep. QB5's almost never pan out, and JJ McCarthy was drafted higher than any QB5 in history. Combining those odds with the expectations of a top 10 overall pick is a massive organizational failure by the Vikings front office. I don't want to say the kid never had a chance, but the historical odds of his draft position show that he only had an 8% chance of becoming a franchise QB, a 4% chance of becoming a star QB, and a 2% chance of winning a Super Bowl as a starting QB. I've heard so much about patience, sample size, development, and age when it comes to this McCarthy conundrum. Not nearly enough is being said about his draft position. Even if Kevin O'Connell was a miracle worker and the greatest QB developer of all-time, the odds of him developing a star QB from the QB5 slot is less than 5%. Only 2 QB's drafted 5th at their position in the last 45 years turned out to be stars (Lamar Jackson and Jalen Hurts). Only one (Hurts) won a Super Bowl as a starting QB. Beyond those two outliers, the best QB5 outcomes like Marc Bulger, Andy Dalton, and Matt Schaub were borderline franchise QB's at best. The Vikings made the exact same mistake with Christian Ponder (QB4 at #12). If you're not in position to take QB1, the worst thing you can do is get desperate and talk yourself into QB4 or QB5. History says that has an astronomically low probability of turning into a franchise quarterback. If the Vikings want to find their franchise QB in the draft, they need to make sure he’s the consensus QB1. Trade up or don’t draft one at all. Funny side note: Kevin O'Connell was also the 5th QB taken in his draft, and he didn't pan out either. * Also for those asking, why 3 Pro Bowls for franchise designation? With the recent trend of star QB's opting out of the Pro Bowl and borderline / mid QB's replacing them, you can't just go by 1 or 2 Pro Bowls. That would include players like Jacoby Brissett, Mac Jones, Geno Smith, and Tua Tagovailoa. I don't think anybody would classify those guys as true franchise QB's. @PhilMackey @jzulgad @DexsTweets @thorku @SKORNorth
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Final note: If you look at the same table and highlight the Vikings QB selections over the last 45 years, you'll see they've never drafted a QB higher than QB3 (Teddy Bridgewater). They are 0/1 on the QB3 slot, 0/2 on the QB4 slot, and now likely 0/2 on the QB5 slot. Can't keep taking low percentage swings when the best QB's are already off the board. It's a terrible strategy that perfectly explains why the Vikings haven't had a true franchise QB in almost half a century. @PhilMackey @jzulgad @DexsTweets @thorku @SKORNorth
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Episode 10 just dropped! youtu.be/oQEi5nbW9bU Audio Note: We had a technical issue with echo during the introduction, but it improves as the video goes along—thanks for bearing with us! In our tenth episode of the Sport Analytics (@SportAnaIytics) Podcast, our host Amrit Vignesh (@avsportsanalyst) chats with Alexander Rogers, currently a Football Analyst for the Tennessee Titans (@Titans). Alex’s journey stretches from playing collegiate football at Drake University (@DrakeUniversity) to refining his data chops as a senior analyst at Hy-Vee, Inc. (@HyVee), then diving into NBA strategy and research with the Phoenix Suns (@Suns) before making the jump to the National Football League (@NFL). He walks us through how balancing on-field responsibilities at Drake honed his discipline and communication, why short, visual insights often matter more than long reports in a busy NFL front office, and how next-generation college tracking data shapes draft prep and player evaluations. If you’re curious about blending a gridiron background with analytics, this episode offers the real-world roadmap from college ball to professional R&D. Key Takeaways: From Gridiron to Analytics: How a collegiate playing career helps interpret data and speak coaches’ language—though it’s not a must-have. Season vs. Offseason: Short-term coach-focused insights in-season vs. deep R&D and scouting analysis after the final whistle. Hybrid Skills in Action: Learning to handle everything from data engineering to front-office stakeholder questions at a “startup-like” NFL analytics group. Visuals & One-Liners: Why color-coded dashboards and quick-hitting metrics turn heads and drive decisions at the highest levels. Strategic Networking: Tips on building a standout portfolio, refining cold outreach, and showing teams you’re ready to contribute. 🔔 Subscribe to our channel for more episodes featuring leaders in sports analytics, career insights, and technical deep-dives! 📧 For inquiries or collaborations, contact me at dave@sportanalytics.com. đŸŽ” Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid. #SportsAnalytics #FootballAnalytics #NFL #NFLanalytics #TennesseeTitans #CollegeFootball #DataScience #PlayerTracking #NBA #PhoenixSuns #Visualization #CareerAdvice #DrakeUniversity
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Dave Yount retweeted
INTRODUCING: Introduction to Basketball Analytics in R by @SportAnaIytics! We are really excited to present you this course by @avsportsanalyst which goes over introductory techniques in data science and statistics applied to basketball datasets. You will learn the concepts of data visualization, introductory machine learning techniques, linear regression, logistic regression, etc. You won't only be presented with ~7 hours of valuable video content but be able to apply what you learn through personal projects which you can add to your portfolios (check out the SportAnalytics portfolio option!). You will also be presented with a certification in the end which can be useful for any internship and job applications in the basketball, or even the general sports analytics industry. We are really excited to finally launch this and hope to see great feedback on the course! sportanalytics.com *If you are interested in football as well, check out @JonahAnalytics's new Introduction to Football Analytics in R course 👀 #SportAnalytics #Basketball #BasketballAnalytics #DataScience #Statistics
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Dave Yount retweeted
INTRODUCING: Introduction to Football Analytics in R by @SportAnaIytics! We are really excited to present this course by @JonahAnalytics which goes over introductory techniques in data science and statistics applied to football datasets. You will learn the concepts of data visualization, introductory machine learning techniques, linear regression, logistic regression, etc. You won't only be presented with ~7 hours of valuable video content but be able to apply what you learn through personal projects which you can add to your portfolios (check out the SportAnalytics portfolio option!). You will also be presented with a certification in the end which can be useful for any internship and job applications in the football, or even the general sports analytics industry. We are really excited to finally launch this and hope to see great feedback on the course! sportanalytics.com *If you are interested in football as well, check out @avsportsanalyst's new Introduction to Basketball Analytics in R course 👀 #SportAnalytics #Football #FootballAnalytics #DataScience #Statistics
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Dave Yount retweeted
INTRODUCINGđŸ„ł: Introduction to Basketball Analytics in R by @SportAnaIytics #SportsAnalytics #Basketball #BasketballAnalytics
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Dave Yount retweeted
Big news at @SportAnaIytics! We have officially released our first two courses: Introduction to Basketball Analytics with R taught by @avsportsanalyst and Introduction to Football Analytics with R taught by @JonahAnalytics! #SportAnalytics #Football #Basketball #DataScience
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Dave Yount retweeted
Big news at Sport Analytics! We have officially released our first two courses: Introduction to Basketball Analytics with R taught by @avsportsanalyst and Introduction to Football Analytics with R taught by @JonahAnalytics! We are really glad to release these courses which will teach you about various introductory techniques (visualization, machine learning, linear regression, logistic regression, etc.). As part of this course, you don’t only receive valuable video content from our esteemed instructors but also are provided with a basis for valuable personal projects which you can use as part of your portfolios when applying for various internships and jobs within the sports analytics industry! The dashboard also has an option for you to create your own portfolio which allows you to showcase any projects you would like to highlight, whether it is associated or not associated with the SportAnalytics organization. We anticipate on releasing our intermediate courses in both football and basketball analytics soon, so if you buy our introductory course in one of those sports, you can buy the intermediate course as part of a bundle! At the end of the courses, you will receive a certification which you can also highlight when trying to break into the industry. We are very excited to witness the growth of this organization and can’t wait to hear feedback from students after finishing their respective courses! #SportAnalytics #DataScience #Statistics #Football #FootballAnalytics #Basketball #BasketballAnalytics
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Dave Yount retweeted
some big news coming from @SportAnaIytics soon 👀
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Dave Yount retweeted
13 Feb 2025
I had a great time talking with @avsportsanalyst, thanks to @DavesAnalytics for the invite! Check out the podcast (podcasts.apple.com/us/podcas
) or video (youtube.com/watch?v=SsXWS3tJ
) - we covered a lot in this! #sportsanalytics #BigDataBowl #datascience
Episode 9 just dropped! youtu.be/SsXWS3tJhko In our ninth episode of the Sport Analytics Podcast (@SportAnaIytics), our host Amrit Vignesh (@avsportsanalyst) sits down with Dr. Ron Yurko (@Stat_Ron), an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University (@CarnegieMellon) and the Director of the Carnegie Mellon Sports Analytics Center. Ron’s path spans an internship with the Pittsburgh Pirates (@Pirates), a data science role at Zelus Analytics (@ZelusAnalytics) (now @Teamworks), and a PhD at Carnegie Mellon—where he now guides the next wave of analysts, develops open-source tools, and co-organizes the annual CMU Sports Analytics Conference. Ron highlights how his early experience manually tracking on-field data at PNC Park fueled his passion for large-scale research, why Python vs. R matters less than mastering the foundations of statistical methods, and how competitions like the @NFL Big Data Bowl serve as springboards for building a public portfolio. From uncovering hidden signal in high-dimensional data to exploring the new frontier of pose-based biomechanics, Ron shares his vision of where sports analytics is heading—and how students, researchers, and industry pros can stay ahead of the curve. Key Takeaways: Finding Your Path: How a cold email, an unexpected project, and relentless curiosity launched Ron’s sports analytics career. Big Data Bowl & Beyond: Why public competitions with tracking data are driving the next wave of innovative research. Academic Industry Mix: Lessons from balancing advanced methodology with the day-to-day needs of teams and clients. Pose Data is Next: From raw player tracking to full skeletal modeling—what emerging data sources mean for future insights. Methodology Over Tool Wars: Why understanding model assumptions and uncertainty trumps any single programming language. 🔔 Subscribe to our channel for more episodes featuring leaders in sports analytics, career insights, and technical deep-dives! 📧 For inquiries or collaborations, contact Dave Yount (@DavesAnalytics) at dave@sportanalytics.com. đŸŽ” Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid (@goodkidband). #SportsAnalytics #DataScience #CarnegieMellon #MLB #NFL #NFLBigDataBowl #MachineLearning #AI #AnalyticsEducation #CareerAdvice #Academia #Innovation
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Dave Yount retweeted
Episode 9 just dropped! youtu.be/SsXWS3tJhko In our ninth episode of the Sport Analytics Podcast (@SportAnaIytics), our host Amrit Vignesh (@avsportsanalyst) sits down with Dr. Ron Yurko (@Stat_Ron), an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University (@CarnegieMellon) and the Director of the Carnegie Mellon Sports Analytics Center. Ron’s path spans an internship with the Pittsburgh Pirates (@Pirates), a data science role at Zelus Analytics (@ZelusAnalytics) (now @Teamworks), and a PhD at Carnegie Mellon—where he now guides the next wave of analysts, develops open-source tools, and co-organizes the annual CMU Sports Analytics Conference. Ron highlights how his early experience manually tracking on-field data at PNC Park fueled his passion for large-scale research, why Python vs. R matters less than mastering the foundations of statistical methods, and how competitions like the @NFL Big Data Bowl serve as springboards for building a public portfolio. From uncovering hidden signal in high-dimensional data to exploring the new frontier of pose-based biomechanics, Ron shares his vision of where sports analytics is heading—and how students, researchers, and industry pros can stay ahead of the curve. Key Takeaways: Finding Your Path: How a cold email, an unexpected project, and relentless curiosity launched Ron’s sports analytics career. Big Data Bowl & Beyond: Why public competitions with tracking data are driving the next wave of innovative research. Academic Industry Mix: Lessons from balancing advanced methodology with the day-to-day needs of teams and clients. Pose Data is Next: From raw player tracking to full skeletal modeling—what emerging data sources mean for future insights. Methodology Over Tool Wars: Why understanding model assumptions and uncertainty trumps any single programming language. 🔔 Subscribe to our channel for more episodes featuring leaders in sports analytics, career insights, and technical deep-dives! 📧 For inquiries or collaborations, contact Dave Yount (@DavesAnalytics) at dave@sportanalytics.com. đŸŽ” Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid (@goodkidband). #SportsAnalytics #DataScience #CarnegieMellon #MLB #NFL #NFLBigDataBowl #MachineLearning #AI #AnalyticsEducation #CareerAdvice #Academia #Innovation
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Episode 9 just dropped! youtu.be/SsXWS3tJhko In our ninth episode of the Sport Analytics Podcast (@SportAnaIytics), our host Amrit Vignesh (@avsportsanalyst) sits down with Dr. Ron Yurko (@Stat_Ron), an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University (@CarnegieMellon) and the Director of the Carnegie Mellon Sports Analytics Center. Ron’s path spans an internship with the Pittsburgh Pirates (@Pirates), a data science role at Zelus Analytics (@ZelusAnalytics) (now @Teamworks), and a PhD at Carnegie Mellon—where he now guides the next wave of analysts, develops open-source tools, and co-organizes the annual CMU Sports Analytics Conference. Ron highlights how his early experience manually tracking on-field data at PNC Park fueled his passion for large-scale research, why Python vs. R matters less than mastering the foundations of statistical methods, and how competitions like the @NFL Big Data Bowl serve as springboards for building a public portfolio. From uncovering hidden signal in high-dimensional data to exploring the new frontier of pose-based biomechanics, Ron shares his vision of where sports analytics is heading—and how students, researchers, and industry pros can stay ahead of the curve. Key Takeaways: Finding Your Path: How a cold email, an unexpected project, and relentless curiosity launched Ron’s sports analytics career. Big Data Bowl & Beyond: Why public competitions with tracking data are driving the next wave of innovative research. Academic Industry Mix: Lessons from balancing advanced methodology with the day-to-day needs of teams and clients. Pose Data is Next: From raw player tracking to full skeletal modeling—what emerging data sources mean for future insights. Methodology Over Tool Wars: Why understanding model assumptions and uncertainty trumps any single programming language. 🔔 Subscribe to our channel for more episodes featuring leaders in sports analytics, career insights, and technical deep-dives! 📧 For inquiries or collaborations, contact me at dave@sportanalytics.com. đŸŽ” Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid (@goodkidband). #SportsAnalytics #DataScience #CarnegieMellon #MLB #NFL #NFLBigDataBowl #MachineLearning #AI #AnalyticsEducation #CareerAdvice #Academia #Innovation
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Thanks to everyone who helped the Sport Analytics Podcast reach our goal of 100 subscribers on YouTube! We also got a lot of new subscribers on Spotify and Apple the last couple weeks. Stay tuned for more incredible guests in the #SportsAnalytics industry! Our 9th episode will be dropping on Thursday. If you haven't subscribed yet that would really help us out. You can do that at the following links: YouTube: youtube.com/@SportAnalyticsP
 Spotify: open.spotify.com/show/6JuluX
 Apple: podcastsconnect.apple.com/my
 Amazon: music.amazon.com/podcasts/1b
 iHeartRadio: iheart.com/podcast/269-sport
 GoodPods: go.goodpods.com/ljZG1C
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Dave Yount retweeted
Episode 8 just dropped! youtu.be/k8umwrrYMGE In our eighth episode of the Sport Analytics Podcast (@SportAnaIytics), our host Amrit Vignesh (@avsportsanalyst) sits down with Yeshayah Goldfarb, the former Vice President of Baseball Operations and Vice President of Baseball Resources & Development for the San Francisco Giants (@SFGiants). Over a remarkable 24-year tenure—and three World Series rings—Yeshayah rose from intern to executive, pioneering new analytics systems, spearheading player development programs, and championing flat leadership to unify scouts, coaches, and data teams. Yeshayah shares how he integrated emerging technologies (like @Hawkeye_view), leveraged minor-league free agent signings to assemble championship depth, and built strong rapport with front-office, on-field, and international staff alike. Whether you’re intrigued by scouting international prospects in Venezuela or shaping a multimillion-dollar roster for a Major League team, this episode explores the daily hustle, strategic foresight, and passion that powered one of Major League Baseball (@MLB)’s most successful front offices in recent history. Key Takeaways: From Intern to Executive: How relentless curiosity and “doing every job” can set the stage for career-long success. Analytics & Scouting Harmony: Why building trust and customizing data for each role drives organizational buy-in. Strategic Depth: The hidden impact of minor-league free agent gems like Gregor Blanco and Yusmeiro Petit. Budgeting for Innovation: How proactive R&D investments and being a “first mover” in new technology can pay dividends. Advice for Aspiring Analysts: Gain experience today—no permission needed—by scouting games, honing R/Python/SQL, and cultivating a learner’s mindset. 🔔 Subscribe to our channel for more episodes featuring leaders in sports analytics, career insights, and technical deep-dives! 📧 For inquiries or collaborations, contact Dave Yount (@DavesAnalytics) at dave@sportanalytics.com. đŸŽ” Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid (@goodkidband). #SportsAnalytics #BaseballAnalytics #BaseballOperations #SanFranciscoGiants #MLB #DataScience #Moneyball #PlayerDevelopment #CareerAdvice #FlatLeadership #SportsTech
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