#KPI Blog: Recapping the #KPI

#KPI 1.0 is relatively complete now.  And what a year it was.

My main intent for finally beginning to publicize the #KPI data was to see if I had “something” or “nothing”.  I had been utilizing the formula on college basketball rankings while compiling data and making schedules behind the scenes for close to a decade. As the rankings were taken more seriously in private, I became more curious.  What did I have? Was it a fun hobby or was it a resource with value?

Slowly but surely, I have learned that I have “something” and am refining the scope of what that “something” is.  I held back a significant amount of data and information partly because I wanted to see what worked and what didn’t work to drive interest and partly to stay clear from any perceived bias coming from my role at Michigan State.  This first year served very much as a trial run and was a huge success with views in the tens of thousands. Hits on the site peaked as Selection Sunday approached. Thank you for making me feel so good about my decision.

I’ll also admit that while I had a written plan going into last fall, I strayed from the plan as I learned what people were reading most.  Ironically, I had no intention of tracking college basketball statistical trends (or more specifically the”foul/free throw data”) to the extreme level that I did.  Those updates proved invaluable for many and helped drive the national conversation throughout the season.  That data opened doors and built contacts I never saw coming. How I compiled the data in the existing and exhaustive ranking database also opened up literally millions of new data points that proved invaluable and provided me with plenty of smiles and ‘aha’ moments.  Studies on scoring differences based on afternoon or evening games in college basketball, scheduling trends in college football and travel distances affecting expected output are now possible and relatively simple, and that is just the tip of the iceberg.

The “Schedule” worksheet in the 42.6 MB Excel file tracking data on the 5,947 college basketball games involving a D-I team has grown to contain 2.3 million cells of data.  That’s one worksheet in one sport.  I am working toward a more user-friendly way to both compile the data and allow people to access it (rather than PDF files and Word-based blog entries). I’m hopeful readers will be have the ability to easily customize data in the future and am hopeful the followers continue following.

The Future of the #KPI is exciting.  The formula is capable of ranking teams and games in any sport that has a result and a score.  I need to further automate the data to allow for rankings in more sports, both college and professional (ahem, computer programmer, maybe?).  Admittedly, I entered a LOT of numbers manually this past year before I could let Excel do its thing. With all due respect to the RPI that has been used for more than 30 years, I believe the #KPI is more accurate as an overall ranking system while also providing more specific data for game by game analysis.  The ability to decipher quality wins and identify outliers is invaluable. With the football committee starting this fall, data will be critical as the smallest details will separate the fourth and fifth best teams.  While the RPI only gives a final “what”, the #KPI gives what, why and how come.  The #KPI can answer questions the RPI can’t.

I am also running analysis to possibly make some slight enhancements to the formula.  In order to improve accuracy, I’m playing with the home/away/neutral adjustments in order to possibly use percentile-based scaling of the adjustments based on the level of opponent (rather than a set adjustment and scaling for the strength of opponent only later).  This would emphasize the quality of opponent slightly more than it is now.  I’m also toying with some data relative to margin as well.  Currently, margin is factored as a multiplier based on twice the percentage of total points scored.  I want to minimize some extreme outliers. The same formula is used across multiple sports.  I’m debating that topic with myself as well. (Suggestions are always welcome at kpauga@gmail.com). Once I am comfortable that the formula is properly protected, I will be releasing the exact formula and rationale.

#KPI Sports Scheduling has always been a serious passion of mine and has always been part of the long-term plan of the #KPI brand.  Schedules can be made for both competitive and geographic equity.  I use computers but also incorporate some of the permutations by hand in order to create the perfect schedules.  Scheduling is a fine art more than simply plugging teams into a rubric.  Television, team travel, rivalries and so much more can be factored depending on a conference or league’s preferences. Thank you to those who have already given me the opportunity to prove I can bring value to their schedules (wink, wink).  Think I’m crazy?  Send me your data and I’ll produce a schedule for you.  I’m not kidding.  My goal remains to create the Major League Baseball schedule someday soon – the most complicated and largest undertaking in professional sports.

There were plenty of fun, anecdotal moments from the #KPI throughout this year too.  Waking up on November 11 to the initial foul/free throw/scoring data going viral was a surprise. Watching the Arizona-San Diego State game the following Thursday and seeing “KPI” on a major TV network was special.  Determining the math by hand as to how big an outlier the Boston College-Syracuse score was on the back of an itinerary while sitting on the floor of a West Lafayette hotel was unique.  There were consecutive all-nighters at the Big Ten Tournament getting data updated after west coast games were done and my MSU responsibilities were completed for the night.  Numerous texts and calls came that last weekend asking “where is my team in the KPI?”  My response was “I’m glad you care!” Answering Tracy Wolfson’s question during warmups of the Big Ten Tournament championship game and telling her I thought MSU would be a “4 seed in Spokane” only to have it come true made for a few laughs. There were several Twitter alerts on my phone with new followers I was honored to have and won’t take for granted. All in all, it was incredibly fun and challenging.

Thank you to the readers, the followers and those who have given invaluable advice during this first year (my favorite coming in December when I was told to keep it simple and at a level where an average basketball fan can understand the numbers).  I’m humbled as to how people have taken it seriously and have come along for the ride.

I will be working this summer in preparation for #KPI 2.0 in August (and of course, suggestions are always welcome).  I’m excited to see what the future will bring…

THANK YOU from the #KPI

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