Welcome in. It is really fantastic to have you
here with us today. Yeah, it's great to be here.
Whether you are prepping for a major presentation
or trying to catch up on a totally new field,
or honestly, if you are just insanely curious
about how the underlying systems of the world
actually work. You have come to the exact right
place. The absolute best place. We have a highly
focused mission for this deep dive. Today we
are exploring the really fascinating and sometimes
hidden intersection of advanced mathematics and
men's college basketball. Which is quite the
combination. It is. And we are going to do it
by extracting the most crucial insights from
a single source. which is a comprehensive breakdown
of the Pomeroy College basketball ratings. And
I actually love the visual we have going on right
behind you. Well, yeah, the glowing basketball
court. Yeah, with all those complex mathematical
equations and scrolling statistics overlaid on
the hardwood, it perfectly sets the mood. I am
very ready to dig into this with you because
we are looking at a space where the visceral,
highly emotional reality of a basketball court
collides directly with pure abstract mathematics.
It's a perfect study in how data can completely
upend traditional wisdom. Absolutely. And before
we jump in, I just want to set the table for
you. This is a deep dive. We are taking this
specific source material, stripping away the
noise, and pulling out the absolute most important
concepts for you. Just the essentials. Exactly.
We are looking at how a niche mathematical model
revolutionized the way analysts, media heavyweights,
and fans understand one of the most unpredictable
sports in America. Okay, let's unpack this. Let's
do it. We have to start at the very beginning
with the origins of the system. The ratings are
essentially a series of predictive models for
men's college basketball teams created by Ken
Pomeroy. But the detail that really stands out
to me is the timeline. The timeline is key. These
ratings were first published online in the year
2003, and they were completely free of charge.
Think about the internet and sports analysis
back in 2003. We were still dealing with dial
-up in a lot of places. Yeah, that 2003 timestamp
is vital context. If you think about the landscape
back then, we were definitely not saturated with
the kind of advanced metrics we see today. The
analysis of the era was heavily, heavily reliant
on the eye test. The eye test? Literally just
what a scout or a coach or a columnist felt when
they watched a team play. It was dominated by
subjective narrative. Right. It was all about
momentum and heart and... Who wants it more?
Exactly. Those classic who wants it more arguments.
You'd hear on sports talk radio constantly. The
primary data points people relied on were simple
box scores, you know, points, rebounds, assists,
and obviously a team's overall win loss record.
Right. So into that very specific. somewhat archaic
environment steps, Ken Pomeroy publishing a highly
sophisticated predictive mathematical model online
for anyone to access. It was essentially democratizing
high -level sports data before that was really
a thing. It was a massive leap forward because
what he was publishing wasn't just a slightly
better way of counting basic stats. He was introducing
a fundamentally different way to evaluate performance.
Well, the core mathematical engine driving the
entire system is based on something called the
Pythagorean expectation, which Pomeroy specifically
adjusted for the unique nuances of college basketball.
Now I have to play devil's advocate here for
a second. Go for it. If you were following a
college basketball season, the ultimate goal
is to win games. If a team is 25 and 5, they
are winning. Why do we need a complex Pythagorean
expectation to tell us how good they are? Isn't
the win -loss record the ultimate indicator?
What's fascinating here is that the math actually
proves a win -loss record can be incredibly deceptive.
Really? Yeah. If we look at the concepts tied
to this model, specifically terms from the source
like sabermetrics and win probability, we see
a total paradigm shift. Traditional evaluation
just looked at the final score, but Saber metrics
and the Pythagorean expectation look under the
hood. They look at the engine. Exactly. They
evaluate the underlying metrics. Specifically,
they look at the ratio of points a team scores
compared to the points they allow over time.
Okay, so point differential. But why is that
inherently better than just looking at wins?
Because a simple win -loss record doesn't account
for luck or variance or the actual margin of
victory. Imagine a team that wins five games
in a row by a single point. maybe off some lucky
buzzer beaters. Sure. Their record looks flawless,
but the math looks at their point differential
and realizes they are living on a knife's edge.
Now compare them to a team that loses one game
by two points, but wins their next four games
by 30 points each. Ah, I see where you're going
with this. Right. The second team has a technically
worse win -loss record, but the Pythagorean expectation
reveals that they are vastly secure in their
possession -by -possession efficiency. So it
exposes the paper tigers of the sport. It moves
the conversation away from a historical record
of what happened yesterday and transforms it
into a predictive engine calculating what is
statistically probable to happen tomorrow. Precisely.
And the math itself isn't just a simple linear
ratio. The Pythagorean expectation applies exponents
to those points scored and points allowed. Hence
the Pythagorean name. Right, mimicking the famous
formula. In baseball, where this originated,
the exponent was roughly two. And it is really
worth noting that Our source makes it clear Pomeroy
wasn't operating in a total vacuum. He is part
of a very specific lineage of thinkers. He is.
The text names two other noted statisticians
who use variations of this exact mathematical
concept in basketball, Dean Oliver and John Hollinger.
When you see a creator like Pomeroy linked to
figures like Oliver and Hollinger, it establishes
the credibility of the whole movement. These
are the foundational architects of modern basketball
analytics. Pomeroy is taking the analytical frameworks
that were Just starting to gain traction in professional
sports and custom building an engine for the
wildly unpredictable world of college hoops.
Custom building that engine is key because college
basketball isn't the NBA. You don't have 30 teams
playing a standardized 82 -game schedule. Far
from it. You have hundreds of Division I programs,
wildly different levels of talent, and non -conference
schedules that are just all over the map. You
can have all the elegant math in the world. But
if your model doesn't accurately predict the
games in that chaotic environment, it is completely
useless. Exactly. The proof of concept always
has to be in the prediction. Here's where it
gets really interesting. Our source references
a 2011 article from the New York Times, specifically
from Nate Silver's FiveThirtyEight blog, which,
as you likely know, is an outlet famously dedicated
to rigorous data -driven journalism. Oh, absolutely.
In that piece, they reveal a hard data point
that is really the absolute anchor of this deep
dive. According to their analysis in 2011, the
Pomeroy College basketball readings boasted a
73 % success rate in their predictions. That
73 % figure is phenomenal, especially when you
consider the sheer volume and volatility of college
basketball games. It is. But the source immediately
gives us a point of comparison to put that 73
% into perspective. It notes that this success
rate is exactly 2 % better than the ratings percentage
index commonly known as the RPI. Right. Now,
for a long time, the RPI was the gold standard
metric used by the establishment, including the
actual tournament selection committee, to evaluate
teams. Yes. RPI is explicitly listed in the source
under traditional methods and computer models.
It was the entrenched system. So Pomeroy's open
source math beats the establishment's RPI by
2%. I have to push back again here, though. OK.
If you were just glancing at those numbers, 73
% versus 71%, a 2 % margin sounds incredibly
small. You might look at that and think, we are
doing all this complex possession by possession
Pythagorean math, applying exponents to point
ratios just to gain two measly percentage points
over the traditional formula. It is a totally
fair question. But in this specific context,
a 2 % edge is not small at all. It is a monumental
advantage. To understand why, you have to look
at the concepts listed in the text. specifically
strength of schedule and home advantage. And
you have to compare how Pomeroy handles them
versus how the RPI handled them. Let's break
that down. How did the RPI handle it? The RPI
was heavily reliant on just three factors. A
team's winning percentage, their opponent's winning
percentage, and their opponent's opponent's winning
percentage. So it was basically a giant strength
of schedule calculator. Exactly. But... The fatal
flaw was that it didn't care how you played against
that schedule. If you played a team with a great
record and lost by 40 points, the RPI might actually
reward you just because your opponent was highly
rated. It was totally blind to efficiency. That
sounds like a massive blind spot. It was. Pomeroy's
adjusted Pythagorean system corrects this entirely.
His model evaluates efficiency on a per possession
basis. It strips away the pace of the game. So
if a team plays a very slow, deliberate style
and only scores 60 points, but they only had
50 possessions, Pomeroy's math recognizes that
they are incredibly efficient. It then takes
that efficiency and adjusts it based on the defensive
strength of the specific opponent they played.
And it mathematically accounts for whether the
game was played at home or on the road. So how
does that directly translate to the 2 % advantage?
Think about it like the house edge in a casino.
A casino doesn't need to win 90 % of the hands
at a blackjack table to be wildly profitable.
They just need a mathematical edge of a couple
of percentage points over the player. Right,
because of the volume. Yes. Over the course of
thousands of hands, that tiny edge translates
into an insurmountable advantage. The exact same
principle applies here. Over the course of a
massive college basketball season, with literally
thousands of individual games being played consistently
beating the established metric by 2%, means your
underlying model is capturing reality far more
accurately than anything else out there. It means
your math is successfully quantifying the impact
of a brutal road environment or the true efficiency
of a slow -paced team in a way that traditional
RPI was completely missing. Yes. That 2 % edge
is the mathematical proof that Pomeroy's adjustments
for pace schedule and home court are working.
It is why data journalists at the New York Times
were paying such close attention. He built a
better mousetrap. And the source groups these
rating systems into different categories, which
sets up a really fascinating contrast in how
we evaluate sports as a whole. If we connect
this to the bigger picture, we see a fundamental
clash of evaluation philosophies detailed right
here in the text. Under the sports rating systems
banner, we see Pomeroy listed alongside other
objective mathematical models. But right beneath
that, there is an entirely different category
labeled polls and opinion. This includes the
traditional heavyweights of sports media like
the AP poll and the coaches poll. Ah, yes, the
classic top 25 rankings. The lists that drive
90 % of the debates are on the water cooler and
on sports television. Exactly. And it is crucial
to recognize that we aren't saying one is inherently
good and the other is bad. We are simply looking
at the factual difference between an objective
algorithm and subjective human voting. Which
is vast. Very. The Pomeroy ratings represent
a cold, emotionless mathematical reality. The
algorithm does not care if a team has a legendary
Hall of Fame coach. It doesn't care if a mid
-major program has an incredibly compelling underdog
storyline that everyone is rooting for. It definitely
doesn't - care if a player hit a flashy highlight
reel dunk on national television last night?
It is entirely blind to the narrative. It just
sees the data points. Precisely. It only cares
about the raw data of possessions, offensive
efficiency and defensive efficiency. On the other
hand, the subjective human voting systems like
the AP poll, which is driven by sports writers
or the coaches poll, are entirely based on human
observation. And human observation naturally
incorporates narrative momentum and bias. For
example, human voters are notorious. for penalizing
a top -ranked team heavily if they barely survive
an ugly low -scoring game against an unranked
opponent. Even if they win, the voters drop them
in the polls. Or conversely, human voters might
reward a team for showing incredible grit and
heart in a close loss to a rival. Right. Humans
judge the aesthetics of the game. A mathematical
model just calculates the adjusted point differential.
What is so valuable for you as someone trying
to gain a deeper understanding of the sport is
recognizing that holding multiple perspectives
gives you a massive analytical edge. You need
both. Yes. If you only look at the subjective
human poles, you get swept up in the hype and
miss the underlying inefficiencies. If you only
look at the math, you might miss unquantifiable
human elements like injuries or team chemistry
issues that aren't yet reflected in the data.
You use the math. to check your inherent human
biases, and you use your human observation to
contextualize the math. That makes total sense.
And what is so incredible to see in this source
material is how rapidly Pomeroy's cold mathematical
approach bled into the mainstream sports media
consciousness. We are talking about an environment
that was deeply entrenched in subjective opinions,
and the math just forced its way in. The citations
in this Wikipedia article serve as a roadmap
of how advanced analytics conquered traditional
sports media. It didn't happen overnight, but
the progression is undeniable. and specialized
data -heavy sites like Basketball Perspectives.
That makes perfect sense. That is his core demographic
of early adopters. The stat heads. Right. But
then the circle expands. You see citations from
ESPN's College Basketball Nation blog. Then it
jumps to mainstream print media, both local and
national. The source lists pieces from the Topeka
Capital Journal all the way to the New York Daily
News. There's even a specific citation from 2010
where the Daily News discusses Dick Fetella,
who is arguably the ultimate embodiment of traditional
emotion -driven enthusiastic broadcasting in
the exact same broader conversation about tournament
selection alongside Pomeroy's metrics. Wow. The
contrast between those two approaches being mentioned
in the same breath is striking. It really is
the old guard meeting the new guard. But for
me, the ultimate flex in these citations are
the articles from the Wall Street Journal. The
source notes multiple pieces written by Carl
Bielek for The Wall Street Journal's Daily Fix.
That is a pivotal crossover moment. When a financial
newspaper improved a publication whose entire
reputation is built on rigorous data analysis,
market efficiencies, and quantitative modeling,
starts routinely citing your college basketball
ratings, you have officially established supreme
credibility. Look at the titles of those WSJ
pieces provided in the references. Preseason
rankings should be heated in Ken Pomeroy's winning
bracket. They aren't treating this as a fun sports
distraction. They are treating his basketball
algorithm with the exact same seriousness and
analytical rigor one might apply to a complex
stock market forecast. Exactly. They recognize
that a 73 percent predictive accuracy is a massive
market inefficiency just waiting to be exploited.
And that leads directly to the ultimate proof
of his elevated status within the sports media
ecosystem, which is explicitly noted right there
in the text. Pomeroy became a contributing writer
for ESPN's Insider Feature. Think about that
arc for a second. We started this conversation
talking about a solitary guy who published a
free experimental mathematical model on a website
back in 2003, right when people were still arguing
over newspaper box scores. Over the course of
the next decade, because that adjusted Pythagorean
expectation proved to be so devastatingly accurate
hitting that 2 % edge over the establishment
RPI. It completely evolves. The free math becomes
premium, highly sought after expertise locked
behind the subscription paywall of the biggest
sports broadcasting network on the planet. It
perfectly illustrates a core truth about highly
competitive industries. The establishment will
always adopt a new paradigm once its predictive
power is undeniably proven. The traditional media
didn't just ignore the math, they absorbed it.
They recognized that the Pythagorean expectation
properly adjusted for strength of schedule and
home court advantage on a per -possession basis
was simply a better tool. The Nate Silver 538
article titled, In NCAA Tournament Overachievers,
Often Disappoint, really sums up exactly why
the media needed this tool. The math exposes
the overachievers. It strips away the illusion
of a lucky five -game winning streak and reveals
the true underlying strength or weakness of a
team. Exactly. It gives analysts an x -ray vision
that the eye test simply cannot provide. And
once you have that x -ray vision, you can't unsee
the underlying structure. It forces everyone
from the casual fan filling out a bracket to
professional analysts on television to elevate
their arguments. You can no longer just say they
wanted it more. You have to explain how they
overcame a massive deficit in offensive rebounding
efficiency. So what does this all mean? We have
covered a vast amount of ground today, pulling
apart the history, the math, and the impact from
this single source. We took a journey that started
in 2003 with a statistician deciding to apply
an adjusted Pythagorean expectation. to the wildly
unpredictable world of men's college basketball.
We did. We explored how shifting the focus from
simple wins and losses to a deeper per position
efficiency metric allowed this model to outperform
the traditional establishment backed ratings
percentage index by a vital 2 % margin. And we
broke down why that 2 % is actually a massive
casino like edge in a high variance environment.
Exactly. And we traced. How that undeniable 73
% predictive accuracy ultimately forced its way
into the mainstream, changing the way heavyweights
from ESPN to the data journalists of the New
York Times and the Wall Street Journal talk about
analyze and understand the sport. It changed
everything. For you, whether you are trying to
find an edge in your office tournament bracket
or you are just someone fascinated by how quantitative
data shapes our understanding of reality, the
story of the Pomeroy ratings offers a profound
lesson. It proves that if you're willing to look
past surface -level results, like a team's basic
record or the loudest, most confident opinions
on television, and dig into the underlying objective
math, you can gain a measurable, distinct advantage.
The truth of the situation is very often found
in the deeper metrics hidden just beneath the
popular narrative. This raises an important question,
though. We have spent this entire time dissecting
and marveling at a 73 % predictive success rate.
And as we established, hitting that mark in an
environment as notoriously chaotic, emotional
and high variance as college basketball is a
phenomenal mathematical achievement. But by definition,
it still leaves 27 percent of the outcomes completely
unpredicted by the algorithm. If models like
the Pomeroy ratings can successfully strip away
human bias and subjective narrative to achieve
that 73 % accuracy today, will we eventually
build a mathematical model that is so perfectly
tuned and so flawlessly adjusted for every conceivable
physical variable that it actually reaches 100
%? That is the question. Or is there a fundamental,
deeply unquantifiable human element to sports,
a literal madness born of pressure, adrenaline
and sheer random chance that pure mathematics
will simply never be able to solve? That is exactly
the kind of lingering thought we'd love to leave
you with. A massive thank you to you for joining
us on this deep dive today and exploring the
numbers behind the madness. We hope you walk
away looking at the statistics, the narratives,
and the underlying systems in your own life a
little bit differently. Until next time, keep
questioning the accepted metrics, keep looking
for your own 2 % edge, and we will catch you
on the next one.