March Madness has the unfortunate reputation as being one of the most productivity draining events of the entire year for many organizations. Instead of focusing on this phenomenon, which many others have already so eloquently done, (here’s a great one http://bit.ly/GAUuce) this post is about the underlying data and capabilities of it to predict a team’s success in the tournament and how analytics is as important for those of us filling out our brackets as it is for the success of our organizations.
Each year, as NCAA tournament time rolls around, millions of
college hoops fans, inclusive of myself, try to predict which lower seeds will
upset a higher seed, which school will make a Cinderella run and who will
ultimately make the final four.
I bleed Orange and Blue. This means my beloved Florida
Gators are a lock for the final four every year and traditionally, I’ve always
put them through to the championship game in my bracket.
However, as the landscape was dominated this year by the
likes of Kentucky (who we as Gator fans endured three painful loses to),
Syracuse and Missouri, which received an untimely exit compliment of Cinderella
Norfolk State, I decided to take a more objective approach to filling out my
brackets. I did a side-by-side comparison of how teams would perform in the
tournament based on two key data items used by the NCAA men’s basketball
selection committee.
The first was RPI which is a computer ranking of a team based on the opponents it has played
during the season. The RPI is made up of a complex formula assigning weightings
to things such as winning percentage, strength-of-schedule, wins at home vs.
road wins, and opponents winning percentage. Fortunately for college basketball
fans, the RPI rankings play much less significance (or do they?) in determining
the overall national champion than that other infamous computer ranking system
we all love to hate. Yes, I’m talking about you BCS!
The
second was the team’s won/loss record over their last 10 games. Those we consider experts believe how a team
performed over their last 10 contests, conference tournaments included, is as
good a predictor as any as to whether a team is deserving of being selected for
the tournament field. The thought process here is a team who won 7 of their
last 10 has a much better chance of going deeper in the tournament than one who
may have lost 5 of 6 down the stretch.
And so I
began to fill out my brackets, putting my love for my Alma Mater aside-giving
way to the numbers and higher rationale. As we always say, the numbers don’t lie. In my RPI bracket, the team with the higher
RPI went through to the next round. In
other words, the higher seeded team advanced.
The “last 10” proved a bit more
of a challenge with many teams recording the same won/loss records over their
last ten games. If this was the case, my
tiebreaker was overall number of wins.
Now before
we get to which was a better predictor of who would eventually be crowned
champion, let’s discuss how using your company’s own data can help identify
those employees in your organization who may become champions and those who may
make an early exit.
If I
told you that out of your most recent group of new hires, I could predict with
high levels of accuracy which of those employees would no longer be with your
organization after 1 year, what you would say? How about if I could predict
which of your high performers might consider leaving their current position
with your company, what would you do? I expect you would say “Heath, show me
how I can use my organization’s data to predict employee behavior. “ I’d be
happy to. By creating models that leverage key data points in our HCM systems,
such as salary, length of service, location, job, performance data, we can
successfully predict an individual’s behavior with greater than 80% accuracy.
This is exactly what the selection committee is trying to do when building the
tournament field, creating a model and using readily available data to
determine a team’s success. Although in this case we’re dealing with individuals
and your organization instead of a team and a tournament.
Imagine the value this type of tool would provide to your
organization. Think about reducing employee churn and the cost savings
associated with replacing an employee (estimated in the thousands of
dollars). And how about the ability to
recognize flight risk of a high performer and the potential opportunities
gained if you were ultimately able to retain that individual. The possibilities
are endless. As an HR practitioner, demonstrating these capabilities to your
C-level execs will definitely earn you a seat at the table.
So how’d my brackets do? (ignoring the inherent flaws in my
model) RPI turned out to be a better predictor of success then won/loss record.
A team’s RPI successfully predicted the game winner 62% of the time, including
the overall national champion, the Kentucky Wildcats. Using “last 10” as a
predictor was only accurate in selecting the winner 30% of the time. And my
Florida Gators, who were a dismal 4-6 in their last 10, well they defied the
odds making it through to the Elite 8.
Fortune tellers have their crystal ball, the selection
committee has their RPI. As professionals, we have access to powerful business intelligence tools and data to help
us predict the future. Harness that power to ensure the ongoing success of your
organization. Like we always say, the numbers don’t lie.
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