How often do you rely on a gut feeling when you're
trying to predict how something is going to turn
out? Oh, constantly. We all do it. Right. I mean,
we do it all the time. You're watching a game
and you just confidently declare which team is
going to win the championship based purely on,
well, on the fact that they just look like winners.
Yeah. Or they have that that mysterious momentum.
Exactly. Momentum. And for decades, that highly
subjective, intuition heavy approach paired with
some. frankly, very rigid traditional ranking
systems. That was exactly how we predicted outcomes
in sports. It was the only way, really. But today
we are going to look at how a purely quantitative
math -based approach completely dismantled that
mindset. specifically in the world of men's college
basketball. Which is a massive paradigm shift.
Honestly, when you look at the evolution of how
we process information, this isn't just a sports
story. No, not at all. It's a prime example of
data synthesis and critical thinking in an era
where we are constantly drowning in information
overload. Well said. So the source material we're
working from today is a Wikipedia article detailing
the Pomeroy College basketball ratings. Better
known to most people as Ken Palm. Ken Palm. Exactly.
And our mission for this deep dive is to unpack
how a totally free online rating system revolutionized
predictive modeling, how it disrupted the establishment
and managed to capture the attention of the highest
levels of mainstream media. We're essentially
looking at the jump from niche internet math
to a foundational analytical tool. Right. We're
exploring how someone looked at an established
accepted system, realized it had massive blind
spots, and then used objective math to cut through
all that noise to find a more accurate reality.
Okay, let's unpack this. Let's start with the
origins and the math behind the madness. Sure.
So we're looking at predictive ratings for American
men's college basketball. And these are published
by Ken Pomeroy. Hence the name Ken Palm. Right.
But the most striking thing about this, especially
if we go back to its early days, is the presentation.
Oh, it's so bare bones. It really is. He publishes
these completely free of charge online at KenPalm
.com. And if you visit the site, the logo is
just. brilliantly understated. It's literally
just the text of the URL. Exactly. It's literally
just the logo of the website itself. No flashy
graphics. No aggressive branding. None of that.
It's just a wall of data. And he first started
publishing these ratings way back in 2003. Man,
2003. That is, I mean, that's the dark ages for
mainstream sports analytics. Totally different
landscape. But he wasn't just throwing random
numbers on a wall to see what stuck. There was
a very specific mathematical engine powering
the entire system. Yes. Our source notes that
his sports rating system is based on something
called the Pythagorean expectation. Though Pomeroy
makes some of his own specific adjustments to
it. Right. He had to tweak it for basketball.
Yeah, because if you follow sports analytics,
you probably know that the Pythagorean expectation
originally comes from baseball. Bill James, right?
Yep. It essentially takes runs scored and runs
allowed, runs them through an exponent, and spits
out an expected winning percentage. So Pomeroy
adapted this for college basketball. Right. He
transitioned it to points scored versus points
allowed. And the fundamental logic here is that
a team's win -loss record doesn't always tell
the truth about how good they actually are. That's
the core of it right there. Because a win -loss
record is heavily influenced by luck. The sequence
of games. Or winning several close one possession
games that could have easily gone the other way.
Exactly. A bad call by a ref, a lucky bounce.
The Pythagorean expectation strips all that away.
It calculates what your winning percentage should
be based on your fundamental underlying efficiency.
It's looking under the hood. It really is. It
asks, based on the core mechanics of how you
are outscoring your opponents or being outscored,
what is your true baseline level of performance?
Right. And then Pomeroy added his own adjustments.
He factored in things like the pace of play to
make it highly specific to the college basketball
environment. So it basically separates the signal
from the noise. Precisely. Like you might have
a team that is 10 -0, but they won every single
game by a single point against totally mediocre
competition. They look great on paper. Right,
but they're creeping by. Then you have a team
that is 7 -3, but their three losses were on
crazy last -second shots, and their seven wins
were absolute 30 -point blowouts. Right. And
traditional metrics might rank that 10 -0 team
much higher. But the Pythagorean expectation
looks at the point differentials and identifies
the 7 -3 team as the inherently stronger squad
moving forward. What's fascinating here is that
this mathematical approach wasn't just an isolated
experiment. How so? Well, there's a broader lineage
of advanced analytics converging at this exact
time. Our source points out that variations of
this Pythagorean expectation are also used in
basketball by noted statisticians like Dean Oliver
and John Hollinger. Oh, right. Dean Oliver essentially
wrote the book on basketball on paper, and John
Hollinger created the player efficiency rating
for the NBA. the per exactly so when you see
a pattern of analytical minds independently utilizing
similar mathematical models to evaluate a game
it validates the methodology it proves it's not
just a fluke exactly it demonstrates that beneath
the chaos of a live basketball game the bad referee
calls, the lucky bounces off the rim, there is
a measurable mathematical pulse that dictates
long -term success. Which brings us to the actual
results of all this math, because, you know,
having a theory is one thing, but proving it
works is another. The proof is in the pudding.
Here's where it gets really interesting. We have
a source from a 2011 article in the New York
Times. Specifically, it was written by Nate Silger
on his FiveThirtyEight blog. Oh, Nate Silger,
yeah. A heavy hitter in the analytics world.
Definitely. And in this article, Silver noted
that the Pomeroy College basketball ratings boasted
a massive 73 % success rate. 73%. In college
basketball, that is a highly significant figure.
Let me play devil's advocate for a second, though.
Go for it. 73 % means the model is getting it
wrong 27 % of the time. True. It's missing more
than one in four games. If I took my car to a
mechanic and he fixed it 73 % of the time, I'd
find a new mechanic. Yeah, fair point. So why
was the sports analytics community looking at
a 73 % success rate and treating it like a complete
revolution? Because you have to consider the
baseline of the environment you're operating
in. Yeah. College basketball is inherently chaotic.
Pure chaos sometimes. You're dealing with 18
to 22 -year -old kids playing in highly emotional
environments where variance is just massive.
100 % success rate is mathematically impossible.
Right. In predictive modeling, whether you're
looking at sabermetrics in baseball or the ELO
family of ratings like GLICO or DWZ used in chess,
you are measuring yourself against the prevailing
standard. not against absolute perfection. Ah,
okay. And the prevailing standard at the time
was the RPI. The infamous RPI. Yes. The source
explicitly notes that Pomeroy's 73 % success
rate was 2 % better than the traditional rating
percentage index, or the RPI. Which was huge.
Right, because the RPI was the entrenched official
standard used by the NCAA Tournament Selection
Committee for decades. It was the absolute goliath
of college basketball metrics. It was gospel.
And this raises an important question. To understand
why a 2 % edge is an earthquake in predictive
modeling, we have to look at how the RPI actually
functioned and why Pomeroy's system exploited
its flaws. Let's break that down. So the RPI
was entirely based on winning percentage, your
opponent's winning percentage, and your opponent's
opponent's winning percentage. That is it. It
only looked at who won and who lost. Exactly.
It completely ignored the margin of victory.
Which is a massive blind spot. A fatal flaw.
Think about it. If Team A beats a top -tier opponent
by 40 points, and Team B beats that same opponent
by one point in overtime after a terrible referee
call... The RPI treated both of those outcomes
as exactly identical. Yes. It registered as a
win against a strong opponent for both teams.
No distinction. But Ken Palm's adjusted Pythagorean
math factored in the efficiency and margin of
that game. It recognized that Team A was fundamentally
dominant, while Team B was really fortunate.
So by capturing that nuance, Pomeroy squeezed
out an extra 2 % of predictive accuracy over
the institutional standard? Precisely. And in
the world of win probability and sports rating
systems, 2 % is everything. You know, a 2 % edge
over the RPI might sound like a rounding error
to someone completely outside of analytics. But
I want you to think about this in the context
of your own life or profession. It applies everywhere.
It really does. I know from personal experience,
I used to lose my office bracket pool every single
year because I relied on those traditional metrics.
You and millions of others. I would pick a team
with a shiny 30 -2 record because they look like
winners on it. completely ignoring that they
were barely squeaking by inferior teams. The
classic trap. And then I'd lose to the one guy
in the office who just blindly followed efficiency
data. That 2 % edge in predictive modeling changes
the whole game. In finance, a trading algorithm
that is 2 % more accurate than the market standard
generates billions of dollars. In sports betting,
a 2 % edge is the difference between going bankrupt
and owning the casino. It compounds over time.
It really does. But, you know, when you have
a grassroots math -based system outperforming
the traditional official index, how does the
establishment react? Usually not well. Right.
The RPI was safe. It was institutional. The Pomeroy
ratings represented a disruptive innovation that
fundamentally challenged the established criteria
for evaluating team performance. And historically,
large institutions do not like being told their
legacy systems are flawed by a guy running a
free website. They usually fight it tooth and
nail. They do. But what makes this story unique
is a transition from niche math to a mainstream
media phenomenon. The establishment ultimately
surrendered to the data. They had no choice.
By the 2010s, this predictive model completely
broke out of the math nerd bubble and staged
a full media takeover. The adoption rate among
traditional journalists was staggering. Our source
lists the specific diverse outlets where Pomeroy
is routinely mentioned or interviewed. And the
list shows just how widespread his influence
really became. It wasn't just blogs anymore.
No. We are talking about ESPN's College Basketball
Nation blog. We are talking about SB Nation,
Basketball Prospectus. The big online players.
Right. But also local and regional papers like
the Topeka Capital Journal. Mediate covered him.
And the one that really signals a major shift.
shift. The Wall Street Journal's daily fix column.
That is the one. The jump from sports blogs to
the Wall Street Journal is the critical moment
in this story. It's a totally different audience.
Exactly. When Carl Bialik at the WSJ writes columns
detailing Ken Pomeroy's mathematical approach
to a tournament bracket, it proves the respect
for this data had transcended sports. It moved
into the realm of respected statistical analysis.
Right. The financial sector understands the value
of a 2 % predictive edge better than anyone.
So they immediately recognized the rigor of his
methodology. And he didn't just remain an external
source that journalists occasionally quoted.
He gained true insider status. He became part
of the machine. The source points out he became
a contributing writer for ESPN's insider feature.
His rating system became a standard citation
in major legacy newspapers like the New York
Daily News. It's an incredible trajectory. He
went from a guy tweaking baseball formulas for
college hoops in 2003 to the definitive authority
that the entire sports media apparatus relied
upon. We really need to contextualize why the
media shifted so aggressively toward his model,
too. Because for decades, sports media was built
almost entirely on narrative. Oh, pure storyline.
Broadcasters and journalists rely. it on the
eye test. They'd talk about which team wanted
it more, who had the hot hand, or which coach
just knew how to win in March. Lots of cliches.
But as advanced analytics became more accessible
to the public, those narrative -driven journalists
started getting exposed. Because readers could
just pull up a spreadsheet and prove the columnists
wrong. Exactly. When a legacy columnist claims
a team is a powerhouse because of their winning
culture, but a guy on a blog points out their
adjusted offensive efficiency is ranked 150th
in the country, and they are mathematically due
for a regression. The audience notices who ends
up being right. They do. So to maintain their
credibility, the mainstream media had to adapt.
When the New York Daily News or ESPN cites Ken
Palm, they are signaling to their audience that
they are prioritizing objective data over pure
subjective opinion. It elevated the entire discourse
of college basketball analysis. It proved that
audiences actually crave deeper empirical insights
rather than just relying on the same old barroom
debate talking points. So what does this all
mean? We started by looking at a completely free
website launched back in 2003. Very humble beginnings.
We saw how a single individual took a foundational
mathematical concept. The Pythagorean expectation
tweaked it. and applied it to college basketball
to identify a team's true underlying efficiency
rather than just looking at their basic win -loss
record. Stripping away the luck. Right. We discussed
how this rigorous, data -driven approach achieved
a 73 % success rate, outperforming the NCAA's
deeply entrenched RPI by a crucial 2%. That vital
2%. And finally, we tracked how that undeniable
accuracy forced a media revolution, turning his
independent ratings into a mandatory analytical
tool for everyone, from dedicated sports blogs
to the Wall Street Journal. If we connect this
to the bigger picture, the core lesson is really
about the power of challenging. the default metric.
Yeah. The RPI was the default for decades simply
because it was the way things had always been
done. But it was inherently flawed. Deeply flawed.
Pomeroy proved that progress doesn't always require
inventing something entirely from scratch. Yeah.
Sometimes it just requires applying a better,
more analytical perspective to the data we already
have. Just looking at it differently. Exactly.
It shows that no matter how entrenched the system
is, objective accuracy will eventually win out
over tradition. And that leaves us with a final
thought for you to ponder on your own. We've
seen what happens when one person applies a tweaked
mathematical concept to a traditional index and
completely reshapes mainstream analysis by finding
just a 2 % edge. A small but mighty edge. Look
at your own industry or your own daily workflows.
What established metrics are you currently using
that are just blindly accepted as the standard?
What is your profession's version of the RPI?
And what data -driven adjustment could you make
to uncover the real truth hidden beneath the
surface? Thank you for joining us as we... explored
this shift from intuition to analytics. Keep
questioning the numbers, look for the margins,
and we will see you next time for another deep
dive. Take care.