Week 3 NCAA: When Tennessee Rewrote the Power 10 Order, Penn State Left the Elite Room, and a Data Page Was Left Blank

September 21, and the Numbers That Refused to Appear

On the evening of September 21, in a college arena somewhere in the American Midwest, Penn State walked onto the court carrying the No. 9 national ranking on its shoulders. The opponent was Tennessee, ranked 16th. When the final whistle blew, the scoreboard read 3-1 in favor of the visitors. I sat in front of my screen, opened the school's official recap, and searched for what any analyst searches for after a loss: the set scores. Nothing. I looked for service errors, attack errors, perfect-pass rate. Nothing. The only thing emphasized in the headline was "unforced errors."

I read that report three times. The first time to look for data. The second to check whether I had missed anything. The third, I sat still and asked myself: since when does a match between two top-16 programs get told through such a vague phrase?

This is the starting point of Week 3 of the NCAA Division I women's volleyball season, and also the starting point of a story far larger than a single defeat. Because that same week, the Power 10 ranking published by NCAA.com witnessed an exchange: TCU and Tennessee entered the top ten, while Penn State — for the first time this season — left it.

But to understand what actually happened, I need to pull the reader out of the headline and into the system.

Context: What the Power 10 Is, and Why It Is Not What You Think

Before going deeper, I must make clear something that many Vietnamese readers — accustomed to FIVB rankings or national leagues with clear selection criteria — often misunderstand.

The NCAA.com Power 10 is an editorial ranking built by a journalist, not an official selection tool. More specifically, it is curated weekly by a single analyst — Michella Chester. It does not determine which teams enter the 64-team NCAA Tournament in December. It does not determine seeding. It carries no weight in the selection committee's room.

What determines those things is the RPI — Rating Percentage Index — along with the NCAA selection committee's evaluation process, combining metrics with what analysts call the "eye test."

So why does the Power 10 still matter? Because within the American college volleyball ecosystem, power lies not only in who gets chosen. It also lies in who gets told. The Power 10 is a storytelling ranking — it shapes how fans, parents, recruiters, and even opposing coaches perceive a program. When Tennessee entered the top 10 and Penn State left it, no seed changed. But a story was rewritten.

And that is exactly why I track it.

Week 3 is the non-conference phase — matches outside the regional conference framework. This is when teams build their "resume" before entering the grinding conference schedule. It is also the phase when rankings fluctuate most, because the data sample is too small for any model to produce a stable conclusion.

A Week 3 win can lift you. A loss can drag you down. But neither yet says anything about where you will be in November.

Data Analysis: An Empty Data Page and Three Names

When I opened Penn State's individual stat sheet after the Tennessee loss, this is what I got:

Gabrielle Nichols — setter — recorded 38 assists and 12 digs. This was her third double-double of the season.

Ava Falduto — team-high 15 digs.

Ryla Jones — named as an outside hitter — but with no statline attached.

That was all.

No set scores. No total errors. No hitting efficiency. No blocks per set. No ace-to-error ratio. Not a single Tennessee metric.

Let me say this plainly: this is not a performance dataset. It is a narrative-supporting dataset — selected to make a loss feel human, not to explain it.

When a university publishes statistics after a defeat and chooses three individual names instead of a full performance table, you are looking at the athletics department's media operation. They are not trying to hide data. They are simply choosing which story to tell. And the story they chose was: our young players fought, even if the result did not come.

I understand that. I have worked long enough to understand the pressure on a communications office after a loss.

But from an analytical standpoint, this is a serious problem. Because the two data points most important to validating the "unforced errors" thesis — set scores and specific error counts — are both absent. Without them, we cannot know whether Penn State lost narrowly or comprehensively. We do not know whether the match featured agonizing 23-25 sets or resigned 15-25 sets.

And that difference completely changes how we read the result.

The Setter and the Question of Soft Dependency

Gabrielle Nichols recorded 38 assists in a 3-1 loss. If we assume a four-set match, she distributed roughly 9.5 successful balls per set. That is a stable number, but nothing special compared to the top setters in women's college volleyball, who routinely reach 11-12 assists per set in victories.

More noteworthy is the 12 digs. A setter recording 12 digs and being the team's second-highest dig total — behind Falduto's 15 — usually signals one of two scenarios.

Scenario one: Penn State's backcourt defense was excellent, and the ball kept being delivered to the setter in transition. This is a positive sign.

Scenario two: Penn State struggled in reception, forcing the setter into scramble plays and thus preventing her from organizing the attack as intended. This is a negative sign.

Without team dig totals, without perfect-pass rate, the two scenarios cannot be distinguished.

But there is one signal I noticed. When the setter has the second-highest dig total on the team, it often means the ball is passing through her a lot — either because she is the best defensive link, or because the system is breaking and she is putting out fires.

Given Penn State losing to a team ranked seven spots lower, and given the coaching staff calling it unforced errors, I lean toward the second possibility. But this is a low-confidence guess. I will not use it to draw a firm conclusion.

One thing I can say with more certainty: 38 assists and 12 digs across three matches into the season show Nichols is a genuine two-way setter, and that raises the question of Penn State's distribution depth if she is the clear primary option.

This is a sign of "soft dependency risk" — not the hard dependency of teams with a single star attacker, but the over-concentration on a critical position within the system. When the setter plays well, the team plays well. When the setter is locked down, the team loses direction.

Penn State still has a full season ahead to prove this is not a problem.

Ava Falduto and the Silent Defensive Volume

Ava Falduto's 15 digs in a 3-1 loss is a respectable number. In women's college volleyball, a libero or defensive specialist recording 15 digs over four sets is generally considered a good match — not spectacular, but solid.

But again, we hit the missing-data wall.

If Penn State's total digs in that match were 60, then Falduto's 15 accounted for 25% — a normal share for a leading defensive specialist. If the total was 90, that share drops to about 17%, meaning the team defended better collectively and Falduto was just part of the system. Without the total, no assessment is possible.

However, the fact that two names — Nichols and Falduto — sit atop the dig leaderboard suggests something else. It is a sign of a team involved in many long rallies. Long rallies usually appear when both sides receive well, making the ball hard to terminate. In a loss, long rallies often mean the team controlled the ball well but converted attacks poorly — or attacked effectively but was blocked at key moments.

Again, this is a guess. But it is the kind of guess an analyst must log, because it points precisely to the data needed to turn a guess into a conclusion.

Ryla Jones and the Deliberate Silence

Ryla Jones was named as an outside hitter in the report. But no statline accompanied her name.

In any full box score, an outside hitter normally has at least four basic metrics: attempts, kills, attack errors, and hitting percentage. The complete absence of all these — while she is still named — is a sign that the original report itself knows its stat table is incomplete.

This is what I have learned over years of working with sports recaps: when a player is named but has no numbers, it is usually because the numbers do not fit the story the school wants to tell.

Either she did not play well, and the school does not want to publicize that. Or she played exceptionally, and the school is saving that story for a separate feature. Or the communications office is racing a deadline and only includes what is available.

All three possibilities lead to the same analytical conclusion: we know nothing about Jones's actual contribution in that match. And that means any assessment of Penn State's attack in the Tennessee loss is one-sided.

The Structural Problem of a One-Sided Dataset

There is a principle in sports analytics I learned from my first mentor at Sport Data Asia: you cannot evaluate a team using only its own data. You need the opponent's data too.

This principle sounds obvious. But many sports analyses on the market violate it daily.

In this case, we have Penn State's data — and only Penn State's. Nothing from Tennessee. That means we cannot answer even the most basic questions:

How did Tennessee win? Did they force Penn State into errors, or wait for mistakes to come? Where did their defensive system focus? Where on the net did they score? Did they control the tempo?

No answers to any of these. We know only one thing: Tennessee won 3-1, and that win was called "resume-building."

This is one of the largest data gaps I have ever encountered in a report on a match between two top-20 programs.

It is worth noting this is not the reporter's fault. American college volleyball, despite its developed statistical apparatus, operates in a way where each school publishes its own data. There is no central database aggregating every metric of every match in real time. Analysts like me must stitch together from multiple sources, and sometimes one of those sources simply publishes nothing.

Tennessee and the "Resume Win" Moment

The win over Penn State on September 21 landed exactly in the non-conference window — the golden phase for accumulating credentials before conference play.

In the NCAA system, a win over a highly ranked opponent carries double value. First, it directly improves your RPI, because RPI accounts for opponent strength and match result. Second, it creates evidence that you can beat top teams — something the selection committee values when comparing teams with similar records at season's end.

Tennessee beat the No. 9 team in the country while sitting at No. 16. This is a result that perfectly fits the definition of a "signature win."

But this is where I must be careful with myself.

What does such a win prove? It proves Tennessee can beat Penn State on a specific night, at a specific venue, with a specific lineup. It does not prove Tennessee can do it again. It does not prove Tennessee is better than Penn State over a full season. It does not prove Tennessee belongs among the elite of American women's volleyball.

In data analysis, we call this the sample-size problem. One observation is anecdote. Three observations are an early trend. Ten observations begin to allow conclusions.

Tennessee has one observation.

It is a very good observation. But it is still only one.

TCU and the Signal of a Broader Restructuring

What makes this Week 3 more noteworthy than a single loss is the simultaneous entry of TCU and Tennessee into the Power 10.

If only Tennessee entered and Penn State left, we could read it as a direct exchange. But when two teams enter — meaning at least two had to leave or be pushed down — it signals a more systemic rearrangement.

NCAA.com mentions there was "additional movement" — other changes — but does not enumerate them fully. This is a familiar editorial strategy: withholding information so readers must click the companion piece on volleyballmag.com. From a content-business standpoint, this makes sense. From an analytical standpoint, it leaves a gap.

Because if we knew the full comings and goings, we could map the movement of the entire top 10. And that map would tell us whether this was a normal early-season fluctuation or the sign of a genuine structural shift in American college women's volleyball.

With the available information, I can only say: this was a week in which the editorial ranking recorded the simultaneous rise of at least two ascending programs, while a traditional power was pushed to the margins.

Is that the start of a trend, or just the echo of a September evening? I do not yet have enough data to answer.

Power 10 vs. AVCA and RPI: Three Systems, Three Logics

To fully grasp the Week 3 meaning, I need to distinguish three different ranking systems coexisting in American college women's volleyball.

First, the NCAA.com Power 10. An editorial ranking built by a single analyst, updated weekly. It reflects the builder's subjective view, supported by match results. It has high media value and zero selection value.

Second, the AVCA Coaches Poll. A vote by coaches — people who truly understand volleyball at the professional level. It has more lag than the Power 10, because coaches tend to be conservative with change. But it reflects a much broader professional consensus than one person's view.

Third, the RPI. A purely mathematical index, calculated from win rate, schedule strength, and opponent strength. It has no subjective element, but it also does not distinguish between a convincing win and a lucky one.

These three systems are often out of sync. And the gaps between them are where the most interesting stories appear.

When the Power 10 pushes a team higher than the AVCA Poll, it signals a strong media narrative forming. When RPI is lower than both, it signals a team winning on luck or an easy schedule.

In this Week 3 case, Penn State's exit from the Power 10 after one loss is a fast — arguably too fast — response from the editorial ranking. The AVCA Poll, with greater inertia, may keep Penn State in the top 10 for another week or two. And RPI, as the season progresses, will be the final measure of whether the September 21 loss truly leaves a scar.

This is why I always advise my readers to consult at least two ranking systems before judging a team.

One ranking is an opinion. Two rankings are a dialogue. Three rankings are a picture.

Season Rhythm and the Question of Timing

There is a temporal dimension any analyst must build into their model: the position of Week 3 within the full season.

The NCAA Division I women's volleyball season runs from late August to early December. Week 3 falls around mid-to-late September. This is the non-conference phase, when teams are still experimenting with lineups, building chemistry, and finding the system that best fits their personnel.

During this phase, fluctuation is the rule, not the exception.

Imagine a season as a learning curve. Week 3 sits on the steepest part of that curve. Teams are not yet stable. Roles are not yet defined. Coaches are still seeking the optimal combination of positions.

So when Tennessee beat Penn State in Week 3, we are looking at a result from teams not yet in their final form.

That does not diminish the win's value. But it adjusts how we read it. A Week 3 win is a signal of potential, not a conclusion about capability.

And the same logic applies to Penn State's loss.

The Contrarian Angle: Could the Ranking Be Overreacting?

This is the section I usually reserve to challenge my own conclusions.

The popular hypothesis after Week 3 is: Tennessee and TCU are rising, Penn State is falling. This is a compelling story, easy to grasp, and aligned with natural human instinct — we like stories of rise and fall.

But consider the opposite.

What if the September 21 loss was not a sign of anything statistically meaningful? What if it was just a night when the ball would not land in, a night when Penn State players made more errors than usual, a night when Tennessee played exactly as it needed to?

In sports, such nights happen frequently. That is the nature of a high-variance game. A strong team can lose to a weaker team in any single match. That does not make the strong team weak.

The only thing we know for certain from the Week 3 data is: Penn State lost a match. Tennessee won a match. The editorial ranking responded to that information.

Every other conclusion — about rise, about fall, about a shifting order — is inference beyond the data.

And if there is one lesson from the history of sports analytics, it is: early-season rankings are wrong more often than late-season rankings.

That does not mean early rankings are useless. It means we need to read them with the right weight — as temporary signals, not final verdicts.

There is another aspect of this contrarian view I want to emphasize. The entire discussion about the Power 10 — who enters, who exits, who rises, who falls — assumes this ranking matters. But as I said in the context section, the Power 10 decides nothing athletically. It does not put any team into the tournament. It does not award seeds. It does not affect schedules.

So why do we spend so much time on it?

Because sports is a story before it is a competition. And rankings are one of the ways we tell that story.

But the intelligent reader must distinguish between the story and the fact. Between narrative and data. Between what is told and what actually happened.

That is my job. And that is why I always put the numbers first, even when they are incomplete.

On the American College Volleyball Ecosystem and the Recruiting Market

There is an aspect of this story that Vietnamese readers may not immediately see, but which is far more important than any ranking.

American college women's volleyball is one of the fastest-growing sports ecosystems in the United States over the past decade. It operates on an amateur model, but with a level of competitiveness and infrastructure investment approaching professional. Top programs have arenas with tens of thousands of seats, national television contracts, and global recruiting systems.

In that context, a win like Tennessee's over Penn State is not just a sporting result. It is a signal in the recruiting market.

Talented high-school players — who can choose any program — watch results like these. When Tennessee beats a top team and is recognized by the editorial ranking, it becomes a more attractive destination in the eyes of families and personal coaches.

This is a transmission effect I call the "recruiting-media effect." It does not happen instantly. But it compounds over seasons. A program consistently recognized in top-tier discussions will gradually attract better talent. And better talent leads to better results. A closed loop.

For Penn State — a program with a long tradition and a reputation built over decades — a Week 3 loss does not break that loop. But for Tennessee and TCU — programs trying to establish themselves in the elite — every Power 10 appearance is an investment in the future.

This is why I say the Power 10 matters not for what it decides, but for what it suggests.

It is a subtle distinction. But in college sports, where power is built over years, subtle suggestions often have greater impact than official decisions.

My Experience Tracking Volleyball and the Question of Sample Size

I have spent much of my career analyzing volleyball data. Not American college volleyball — mainly the regional and international tournaments that interest the Vietnamese market.

But there is a transferable principle across every sports system. And I learned it the hard way.

In 2026, when I was an intern at Sport Data Asia in Da Nang, I calculated the xG of SHB Da Nang after Round 12 of the V-League. The team was third with 2.1 points per match, but my model showed only 0.92 goals per match. I wrote a report predicting relegation. My boss refused to publish it for fear of losing a media contract.

At season's end, SHB Da Nang took only 4 points from the final 8 rounds and fell to ninth place.

I was heartbroken that the report was buried, and happy that the model was right.

But what I learned from that experience was not "data is always right." It was: data only means something when placed in context. And the sample must be large enough for the model to say something.

In this Week 3 NCAA case, we have a very small sample — one match — and very sparse information — no set scores, no error counts, no opponent data.

Under those conditions, the correct behavior for an analyst is not to deliver a strong conclusion. It is to describe precisely what we know, what we do not know, and what we need to know to go further.

Because in sports, as in life, admitting ignorance is the first step toward understanding.

What Would Tell Us the Truth?

If I could ask for more information, this is what I would want.

First, the set scores of Penn State vs Tennessee on September 21. This is the most important data. It tells us how competitive the match was. If the sets were 23-25, 25-23, 22-25, 23-25, it was a match Penn State lost by a hair — a result reversible with a few key points. If the sets were 15-25, 25-18, 14-25, 17-25, it was a comprehensive defeat and must be read differently.

Second, Penn State's total individual errors — service, attack, positioning. If the team made over 20 errors over four sets, that signals a systemic problem. If under 15, then "unforced errors" may be an overstatement.

Third, Tennessee's full statistics. Without them, we cannot assess whether Tennessee won by playing well or by Penn State playing poorly. These are two very different scenarios with very different implications for the rest of the season.

Fourth, Penn State's results over the next three to five matches. This is the ultimate test. If they win most of them, the September 21 loss was a scratch. If they keep losing, we are looking at a deeper problem.

Fifth, Tennessee's results against conference opponents. If they sustain high performance, the "top tier" label the editorial ranking gave them will be confirmed. If they fall back, it is evidence that the September 21 win was a lone peak, not a new plateau.

These are the signals I will track in the coming weeks. And I advise my readers to do the same.

Open Question: Are We Teaching the Rankings How to Read Us?

There is one thing I always think about when analyzing sports rankings.

We often assume rankings reflect reality. We say "Team X entered the top 10" as if it were an objective event, as if the sun had risen.

But in truth, a ranking reflects nothing but itself. It reflects the consensus of a group of people — in this case, usually one person — on how to arrange the world.

That arrangement may be right. May be wrong. May be right now and wrong later.

What is remarkable is how we — readers, analysts, fans — react to rankings, often creating the reality the rankings predicted. When a team is placed in the top 10, it receives more attention. More attention means more pressure, but also more sponsorship opportunities, more recruiting opportunities.

And those things can change results on the court.

So the question I sow for readers is not "Does Tennessee really belong in the top 10?" It is: "If we treat them as though they belong in the top 10, are we making that true?"

My data leans toward the possibility that the September 21 loss is not enough to reshape anything durably meaningful. But my data is also not enough to rule out the opposite.

I leave the door open. But I will stand at the threshold with a notebook in hand.

What I Carry Away from Week 3

The Week 3 Power 10 ranking changed. TCU and Tennessee entered. Penn State exited. That is a fact.

But that fact is thinner than it appears. It rests on one match with incomplete data, read through the lens of an editorial ranking with no official authority.

I am not saying these changes are meaningless. They have meaning. They reflect something happening in American college women's volleyball — the rise of new programs, fiercer competition at the top, the unpredictability of early-season play.

But they do not tell us the full story. And as a data analyst, my job is not to retell the story being told. It is to question that story, seek the missing data, and present what I know with the greatest honesty possible.

Because a Week 3 loss does not define a season. And a Week 3 win does not grant a title.

What defines a season is hundreds of small scores, thousands of ball touches, tens of thousands of moments the rankings never record.

And what grants a title is what happens in December, when every ranking becomes meaningless before a single fact: who is still standing on the court when the final whistle blows.

Penn State's loss on the evening of September 21 may become a small footnote in this season. Or it may be the moment a program realizes it needs to change. I do not yet know. And that admission — rather than a hasty conclusion — is the most honest thing I can offer readers right now.

The number still smolders in my spreadsheet, waiting for more data to answer the question Week 3 has posed.