If you weren’t already aware, Bite-Sized Bison is winding down as the College Football Playoff nears (read a letter from me here). If you had a paid subscription, it’s already been downgraded to a free sub, and prorated refunds are making their ways out now. Anything published by BSB from now on will be free to everyone, and all paywalls on previous content have been lifted.
I wanted to offer a chance to answer lingering questions folks might have – about BSB, roster construction, recruiting, leftover analysis, etc. – so I opened up an AMA (Ask Me Anything) recently and fielded some of those questions.
Below is Part 1 of the AMA, which focuses on BSB and data science/analytics. Part 2 will focus on the football-centered questions.
LOL “pending jail sentence.” The short answer is I got a new job — my first job in data after earning my Master’s in Data Science from IU — which demands and deserves my attention in ways that conflict with BSB’s operations and value.
I wish I could explain in simpler, concrete terms why I couldn’t simply use my new skills and pre-existing experience/knowledge (see the farewell letter linked above) to build BSB rather than finding work elsewhere, but the best I’ve come up with is: BSB wasn’t destined to be an outlet for ongoing coverage in the long-term. Could I have chosen to use my Master’s to build this out into something that I could live off of, rather than finding a job outside of sports content creation? Absolutely. But I’ve already lived that life when I was a sports reporter, and that lifestyle was something I pushed against when I originally left the industry. So when that fork arrived in my path with BSB, I was prepared to let go.
Ultimately, I could feel, as recent months progressed, that BSB really was meant to be a temporary outlet while I earned my Master’s. That wasn’t always clear as the newsletter was growing into itself.
I hope everyone fully appreciates local fan-media entities – i.e. CrimsonCast, Assembly Call, among others. This stuff isn’t easy when you have other obligations in life, as most people do. They just make it look easy because they’re talented, hard-working people making sacrifices.
It’s not. BSB means more to me than what I could sell it for.
I worked very hard to provide this content because I genuinely believed in BSB’s mission – “help educate, incubate informed discussion, and offer some new way of watching the games,” as noted in the letter linked above. This built a loyal audience, which is extremely difficult to do in 2025, making BSB valuable as a media product. But I grew to really love Bite-Sized Bison for what it was. and I want it to hold its place in time, as something a small group of people really enjoyed and a connection between me and those people. I don’t want it to be remembered for anything that isn’t associated with that.
The only time money became a factor was when I realized BSB could help pay for my tuition as I earned my Master’s degree. It also helped with access to datasets. If Substack would’ve allowed me to charge less for this, I would have; though, I do believe writers should be paid for their work. In my particular case, if the goal was to make money, I would’ve named the newsletter something less ridiculous.
Like I said in the farewell letter, BSB has “attached its own link in the chain for another creator to attach their link onto, and so forth.” I’m not naive to the fact that BSB’s departure leaves a hole that won’t immediately be filled (I also feel that loss in a way), but I do believe someday, someone will do this work and do it better than I did.
I’m working outside of sports analysis. I’ve begun a position at an airline, where I’m using data to assist in training pilots.
BSB isn’t going away entirely. Like I said in the original farewell letter, this will be a recreational space where I’ll publish projects of interest within sports analysis. Many data folks have side projects like this to keep various skills sharp. Mine just happens to be very public-facing!
An example of the type of content I’m considering would be my capstone project, which explored offensive formations and the transitions an OC makes between them. Which combinations of formations are most productive or successful? Which series of formation types result in the most explosive plays? Which OCs transition the most (or the least) between formations? I was working for months to finish that project and submit it to an MIT conference. I’d like to return to that type of work.
Well, I didn’t know much about data before beginning the program at IU – just what data could do for decision-making and storytelling. I actually was required to take the GRE and complete a Graduate Certificate in Data Science before I could even get into IU’s Master’s program, because I had so little technical experience. So the first two seasons here at BSB (2022 and 2023) were limited to what data I had access to and what was already prepared. The rest required an existing knowledge in the arenas I was talking about, such as what PFF snap counts could show about Tom Allen’s Husky position from season to season. I knew how to tell stories with data, but it was limited by my lack of technical skills.
Simply being introduced to IDEs, APIs, relational databases, programming languages (Python, R, SQL), data types and files, how data pipelines worked, visualization packages, machine learning concepts, among many other things (including being refreshed on statistical concepts), was a massive spring board for me. In a matter of two or three weeks in late-July, early-August 2024, after my third and fourth courses in IU’s DS program, I set up the entire infrastructure for what BSB is today – data sources, data pipeline and lake, recurring visualizations, storytelling structure, etc. Then, I built on that in the year-plus since. I’ll admit, it was a bit frightening at first, because BSB had already built its audience to 1,000 subscribers, and while it’s all familiar to me now, it absolutely wasn’t back then. I’m so proud that I was able to stick that landing, but it didn’t happen without a lot of work. It’s also a testament to the data science skills that can be learned at Luddy.
If I were to choose one significant point that helped me with football analysis, though, it would be how to organize a data project. This is probably where BSB grew the most, as I’ve become more efficient over time. Understanding the life-cycle of a dataset and what is needed before even analyzing is so crucial. I’ve grown much more patient since I began.
BSB’s relationship to my data science path was a sort of higher-stakes sandbox for all of the skills I was learning throughout my Master’s curriculum. When I felt confident in a new machine learning concept, for example, I’d craft something for Bison Bites in the offseason and blend it into in-season content. I did this a lot for new visualizations too, as well as working with a wider breadth of data.
What BSB taught me, in particular, was accessibility. The message is the point. Visualizations quietly take numerous abstract concepts (like all 136 FBS teams’ EPA/play figures) and compress them into one concrete image to communicate a message. Making that image/message as clear as possible is the first step (make it as simple as possible for the message), but some visualizations are designed to spend time with. Some folks can read these, and some can’t. I wanted everyone to feel like they could be a BSB subscriber, so I spent a lot of time writing my observations beneath each visualization. This was a great exercise in fine-tuning my own analysis, of course, but a long-term benefit was also learning what mattered and what didn’t when doing data analysis.
Another point it taught me – just spoke to an IU journalism class about this – is that much of the analysis I was doing (aside from win probability and opponent-adjusted stuff) was descriptive, no matter how deep it got. That means I was describing what had already occurred. I used this to identify patterns and paired it with my own knowledge of football to make educated guesses. The coordinators and players were always in power, not my numbers, meaning Riley Nowakowski was being schemed into position by Mike Shanahan and executing in a way that produced his and the offense’s value metrics. I was doing no more than explaining what they had already done and guessing at what they might do moving forward based on patterns. I feel like that’s a good perspective to take into most data science/analysis situations: intuition and analytics must both be involved to make the optimal decision.
This was always my process as I determined the data or visualizations needed for each bite of a Bite-Sized Bison newsletter. I saw something – either on the field or in the data – that tripped a wire for a particular story that I thought needed to be told and then found the data that either told that story or proved my idea to be a non-story. There was always a network of “stories” behind every BSB newsletter that I hard-wired into a sort of narrative. The Chart was helpful to kick off each week in this way; then I would rabbit-hole my way toward an in-depth weekly preview. Being a former journalist also prepared me for identifying bias in my own stories or in the data. Data analysis is just a hyper-technical version of journalism, in my view.
In terms of egregious stretching of data, the only example that comes to mind is the Strength of Schedule metric used against Indiana’s CFP resume last season. The way ESPN’s FPI SOS metric (a single, often flawed metric) was heavily weighed to determine the quality of Indiana’s 2024 team was frustrating, and if we were to do the same for teams around this year’s CFP, Indiana’s 2024 team wouldn’t look bad (ranked 34th vs. Vanderbilt’s 25th this season, for example, or Notre Dame and Miami’s 44th and 45th). Ohio State was 46th before the Big Ten Championship. I personally like what Brian Fremeau at bcftoys.com does, by assessing how many wins an average team would have against a schedule (and doing this for tiers of the best- or worst-rated teams).
Sports analytics are very fun and interesting. You can learn so many patterns within the sport that pull back the curtain for on-field performance. It’s also one of the best areas to begin learning data science/analysis in general because so much data exists already, often in fairly clean environments. But it’s not easy! I’d recommend the following things for those interested in college football analytics specifically.
Ensure you understand the metrics you’re using. There are too many uninformed folks out there throwing around FPI, SOS, SP+, EPA, and Strength of Record metrics without understanding how they’re built — therefore being unaware of their limitations. Make sure you know as much as you can before using them in your work. Maybe even try building your own EPA formula!
Follow the work of others. All of the data analysts I listed above are a good start for college football, but there are many others for other sports who I follow for inspiration and knowledge. Also, follow data work outside of sports. I found Chartr — a data storytelling site/newsletter — early on in BSB’s life, and if you read through one of their newsletters, you’ll see its influence on this one.
Learn how to code. It’s kind of a joke in 2025 — learn to code, buddy! — but learning R, Python, and SQL unlocked many doors for me in sports analysis. All of my data was pulled and organized via R and Python and queried from my own local database with SQL. All of my visualizations not built in Excel or at Datawrapper (in the early days) were constructed with code. I learned all of these languages in 4 or 5 months back in 2024.
Start with CollegeFootballData.com for your data. This is where many of the best college football analysts get their start because it’s a lot of data and it’s free. I got most of my data from cfbfastr, which is related to CollegeFootballData.com. I created most of my work from their play-by-play data, which covers several levels of college football. One season’s worth of play-by-play data has millions of rows in the table, so you’ll need to know code to manage it. Bill Radjewski, who maintains the site, deserves a lot of credit for what he’s done for so many sports analysts, including me. Other data sources cost money, like Pro Football Focus and Sports Info Solutions, but are good sources when you’re comfortable.
You don’t need to create content. I cannot stress this one enough. I created content because I already had BSB going and was once a professional writer. Creating content for an audience applies a lot of pressure that you wouldn’t have otherwise, and it’s brutal out there; if you’re not good with criticism while you learn, it could be discouraging to your endeavor. I already had lots of experience with this.
Join a community. There are lots of communities out there for others who are doing this exact thing. I’m in the CFB Data Discord (see CollegeFootballData.com), and r/CFBAnalysis has folks doing similar work.
There are many different media for the best college football analysis. I’ll lay out some of my favorites below (though I’m probably forgetting some):
For Indiana Football
CrimsonCast: Of course. I love those guys.
The Big Red Carpet: Rhett Lewis has been the most exciting addition to IUFB media this season. As a former player and current pro broadcaster, he brings a unique set of skills to this particular market that make him a must-listen. He’s on YouTube with the Back Home Network, and he recently began his own Substack similar to CrimsonCast’s.
For data analysis
CFB-Graphs.com: Parker Fleming is one of the leading national college football data analysts right now. He’s building out a whole website that is frequented by most folks in college football. I highly recommend consuming his work if you liked BSB.
CFBNumbers: CFBNumbers helped me out more than once as BSB was getting started. All of his work is free and is structured similarly to BSB. He doesn’t restrict himself to a particular team, focusing on the entire FBS landscape.
GameonPaper.com: I really love this site for raw numbers. My favorite part is that it updates live during games, so if you want to know what Indiana’s success rate is as there is 9:27 left in the third quarter, you can go here and see.
BCFtoys.com: Brian Fremeau does a lot of really great data science work on college football. He’s probably the leader in applying actual data science concepts to college football (aside from maybe Bill Connelly at ESPN), and he created and maintains his own value metric (FEI), which I’d argue is more accurate than FPI.
Bless your Chart: Chris makes a data visualization (with an actual bite-sized accompanying newsletter) that is college sports-related, most often football.
For national analysis and other things
Football Weather: There aren’t a ton of college football podcasts out there where the hosts have been forged in the fires of Indiana Football. That particular disposition lends itself to thoughtful analysis of the sport on a national stage. Galen and Matt offer that. I’ve enjoyed their show as it’s kicked off this season.
Andy and Ari at On3: So many national podcasts get caught up in narratives (Andy and Ari do at times as well), but I worked with Ari on the Ohio State beat. He at least cannot be accused of lacking thought in his takes, and I find them to be an entertaining pairing.
PFF College: Pro Football Focus, whose data I used regularly at BSB, creates its own college football content that is usually interesting.
Trench Warfare: Brandon Thorn is the leader right now in offensive line analysis. He writes a Substack that is not free, but I do recommend subscribing.
Daft on Draft: Indiana fans are going to need to beef up on the NFL Draft now, so I recommend Cory Kinnan’s Substack, Daft on Draft. This is another one that is not free but is worth subscribing to.
Bill and Doug on Ohio State Football: I’ve mentioned them before, but Bill Landis and Doug Lesmerises are two of the most respected beat writers on the Ohio State beat. They’ve been supportive of BSB as well. Would definitely recommend all of their work.
For football knowledge
Coach Dan Casey: This guy really knows what he’s talking about, in terms of football scheme. I haven’t seen anyone on social media crunch as much film as him, and he creates really insightful content based off his observations. I’ve bought both of his books and subscribe to his newsletter, which sends you a play every day.
Coach Martin at Football Advantage: Coach Martin creates content that is mostly focused on the coach’s perspective, primarily for high school coaches. He has some extremely basic blog posts on his site that are helpful for catching up, if you’re newer to football, and he also sends out a daily newsletter that goes deeper into scheme. The newsletters are short and stripped down; all focus is on a short message. I’ve learned several things from him over the years.
Chris B. Brown books: If you’re into books like me, these two Smart Football books are a must. Brown goes deep into the development of various tactics over the years, and I’ve forgotten more than I’ve remembered in the years after reading these. I could return to them and be enthralled over and over.
Throw Deep Publishing: Throw Deep has a ton of videos on YouTube that are helpful for various football concepts, such as running flood concepts or setting up TE screens. Again, this is geared toward coaches, but the best football scheme content always is. They also have entire series on particular schemes, such as the 4-2-5 multiple defense, on their website.
Please comment with other sources you’ve enjoyed! I’m sure other BSB subscribers and I would love to check them out, if we have not already.
Stay tuned for Part 2 of these AMA’s!
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