Decoding NCAA Basketball Rankings: RPI, NET, and KenPom

Every year, the NCAA Selection Committee faces the monumental task of seeding the NCAA Tournament, a decision heavily influenced by complex ranking systems. For decades, the NCAA relied on the RPI, but starting in the 2019 season, a new contender emerged: the NET. This transition wasn’t without its growing pains, sparking immediate controversy among coaches and athletic directors alike. While the NET system has undergone revisions since its inception, it remains the NCAA’s official ranking tool. But what about other influential metrics like KenPom? This article will break down the old reliable RPI, the controversial NET, and the highly respected KenPom, exploring how each system evaluates and ranks NCAA basketball teams.

The Rise and Fall of RPI

The rating percentage index, commonly known as the RPI, is a quantity used to rank sports teams based upon a team's wins and losses and its strength of schedule. It is one of the sports rating systems by which NCAA basketball, baseball, softball, hockey, soccer, lacrosse, and volleyball teams are ranked. From 1981 to 2018, the RPI served as the NCAA’s standard for evaluating teams.

RPI Formula

At its core, the RPI was a straightforward win/loss formula:

RPI = (WP * 0.25) + (OWP * 0.50) + (OOWP * 0.25)

Where:

WP (Winning Percentage): The team’s own winning percentage, calculated by dividing wins by total games played. This accounted for 25% of the RPI. For Division 1 NCAA Men's basketball, the WP factor of the RPI was updated in 2004 to account for differences in home, away, and neutral games. A home win now counts as 0.6 win, while a road win counts as 1.4 wins. Inversely, a home loss equals 1.4 losses, while a road loss counts as 0.6 loss. A neutral game counts as 1 win or 1 loss. This change was based on statistical data that consistently showed home teams in Division I basketball winning about two-thirds of the time. Note that this location adjustment applies only to the WP factor and not the OWP and OOWP factors. Only games against Division 1 teams are included for all RPI factors. As an example, if a team loses to Syracuse at home, beats them away, and then loses to Cincinnati away, their record would be 1-2.
OWP (Opponents’ Winning Percentage): The combined winning percentage of a team’s opponents, contributing a significant 50%. The OWP is calculated by taking the average of the WP's for each of the team's opponents with the requirement that all games against the team in question are removed from the equation. Continuing from the example above, assume Syracuse has played one other game and lost, while Cincinnati has played two other teams and won. The team in question has played Syracuse twice and therefore Syracuse must be counted twice. Thus the OWP of the team is (0/1 + 0/1 + 2/2) / 3 (number of opponents - Syracuse, Syracuse, Cincinnati).
OOWP (Opponents’ Opponents’ Winning Percentage): The winning percentage of those opponents’ opponents, making up the final 25%. The OOWP is calculated by taking the average of each Opponent's OWP. Note that the team in question is part of the team's OOWP. Continuing the example above, a team has played Syracuse twice and Cincinnati once. Syracuse has played one other game and lost, while Cincinnati has played two other games and won. Syracuse has played and beat the team in question (which, excluding the games against Syracuse, only lost to Cincinnati), lost to the team in question (excluding Syracuse, only lost to Cincinnati), and lost one other game (excluding Syracuse, this team has no WP). Cincinnati has played the team in question (excluding Cincinnati, they went 1-1 vs. Syracuse) and won versus two other opponents each of which have no WP when games versus Cincinnati are excluded.

What becomes immediately clear is that a substantial 75% of a team’s RPI was determined not by their own record, but by the performance of their Strength of Schedule (SOS) - specifically, how well their opponents, and their opponents’ opponents, performed. This emphasis rewarded teams playing tougher schedules. For instance, as of now, Boise State sits 18th in RPI at 0.637, narrowly ahead of Utah State at 22nd with 0.629, while Air Force lags at 304th with 0.423.

Criticisms of the RPI

The RPI formula also has many flaws. The RPI lacks theoretical justification from a statistical standpoint. Other ranking systems which include the margin of victory of games played or other statistics in addition to the win/loss results have been shown to be a better predictor of the outcomes of future games. Due to the heavy weighting of opponents winning percentage, beating a team with a bad RPI may actually hurt your RPI. Losing to a good RPI team can help your RPI. Some feel that the heavy emphasis upon strength of schedule gives an unfair advantage to teams from major conferences. Teams from "majors" are allowed to pick many of their non-conference opponents (often blatantly weaker teams). Teams from minor conferences, however, may only get one or two such opponents in their schedules. Also, some mid-major conferences regularly compel their member teams to schedule opponents ranked in the top half of the RPI, which could boost the strength of that conference and/or its tougher-scheduling teams. In basketball, the Missouri Valley Conference has successfully done this: It has become one of the top-rated RPI conferences, despite having very few of its teams ranked in the two national Top 25 polls.

RPI Quadrants

Since 2018, one criterion for determining selection to the NCAA Tournament has been performance against certain RPI quadrants. Typically, a quadrant 1 win is considered a "good win", while a quadrant 4 loss is considered a "bad loss".

Quadrant 1: Home games vs. RPI teams ranked in the top 30; neutral games vs. 1-50; away games vs.
Quadrant 2: Home vs. 31-75 teams; neutral vs. 51-100; away vs.
Quadrant 3: Home vs. 76-160 teams; neutral vs. 101-200; away vs.
Quadrant 4: Home vs. 161-plus teams; neutral vs. 201-plus; away vs.

The NCAA announced on August 22, 2018, that the RPI would no longer be used in the Division I men's basketball selection process and would be replaced by the aforementioned NET.

The NET Ranking: A New Era

With the 2019 season came the NET: NCAA Evaluation Tool. And while the NCAA can royally screw things up, they did nailed the acronym. Unlike the formulaic RPI, the NET is a predictive-learning model, leveraging machine learning to simulate countless game outcomes. It constantly compares expected results with actual outcomes, adjusting its model dynamically.

Read also: Conference Standings Breakdown

Initial NET Factors

Upon its introduction, the NET considered five primary factors:

Team Value Index: A holistic measure considering opponent quality, game location, and the ultimate winner. The remaining factors include the Team Value Index (TVI), which is a result-based feature that rewards teams for beating quality opponents, particularly away from home, as well as an adjusted net efficiency rating.
Net Efficiency: The difference between a team’s offensive and defensive efficiency. The adjusted efficiency is a team’s net efficiency, adjusted for strength of opponent and location (home/away/neutral) across all games played. The NET includes more components than just winning percentage.
Winning Percentage: A straightforward measure of wins versus losses.
Adjusted Win Percentage: This factor heavily weighed game location, rewarding road wins and penalizing home losses.
Scoring Margin: Designed to reward dominant wins, though capped at 10 points to prevent extreme blowouts from skewing results. Scoring margin - While included in the NET, teams receive no extra credit for wins by more than 10 points.

An important note: the NET treats all games equally, regardless of when they are played in the season. A November clash carries the same weight as a February showdown, irrespective of team evolution or injuries. Late-season games from the 2017-18 season, including from the NCAA tournament, were originally used as test sets to develop a ranking model that used machine learning techniques.

The Quadrant System: A Game Changer

One of the most talked-about aspects of the NET is its Quadrant System, which categorizes wins and losses based on the opponent’s NET ranking and game location. You’ll often hear commentators discussing “Quad 1” or “Quad 2” records, and for good reason-these classifications significantly impact a team’s resume.

The quadrants are defined as follows:

Quadrant 1: Home 1-30; Neutral 1-50; Away 1-75
Quadrant 2: Home 31-75; Neutral 51-100; Away 76-135
Quadrant 3: Home 76-160; Neutral 101-200; Away 136-240
Quadrant 4: Home 161-plus; Neutral 201-plus; Away 241-plus

Let’s look at some examples:

New Mexico (currently 46th NET): Holds a 1-2 record in Quad 1 games, 2-1 in Quad 2, and a perfect 5-0 in Quad 3.
Boise State (currently 44th NET): Shows a 2-4 Quad 1 record, 3-0 in Quad 2, and 2-1 in Quad 3.

While Boise State has more losses (4 losses to 8 wins) compared to New Mexico (3 losses to 11 wins), their losses are against higher-caliber, Quad 1 opponents, which mitigates the negative impact on their ranking. New Mexico, despite fewer losses, has fewer Quad 1 wins and a critical Quad 3 loss. A Quad 1 or 2 loss isn’t a “deal-breaker,” but a Quad 3 or 4 loss can severely hurt a team’s tournament aspirations.

The fluidity of these rankings throughout the season is fascinating. A win that starts as Quad 1 can drop to Quad 2 or even 3 if the opponent’s NET ranking falls. This creates an interesting dynamic where teams often find themselves rooting for their past opponents to perform well!

For example:

Nevada, currently 75th, means a game at Nevada would be a Quad 1 opportunity for San Diego State. However, for Utah State hosting Nevada, it would be a Quad 2 game.
If Nevada then loses to Wyoming (86th) at home, that becomes a Quad 3 loss for Nevada, which could lower their overall NET ranking. This, in turn, could downgrade San Diego State’s Quad 1 win against Nevada to a Quad 2, and Utah State’s Quad 2 game to a Quad 3.
Similarly, Utah State’s initial Quad 1 loss to South Florida became a Quad 2 loss after the Bulls dropped to 76th following losses to UAB (110th). If the Bulls lose at North Texas, 151th, and home to East Carolina, 307th, it may become a Quad 3 loses for the Aggies, or South Florida could beat Tulsa, 41st, on the road and go back to Utah State Quad 1 loss. The continuous shifts demonstrate why these rankings are constantly scrutinized.

NET Evolution

Recognizing some of the initial criticisms, the NCAA refined the NET in 2020, dropping winning percentage, adjusted winning percentage, and scoring margin from its core calculations. With the changes announced in May 2020, the NET will no longer use winning percentage, adjusted winning percentage and scoring margin. “When we adopted the NET in 2018, we had reviewed several seasons worth of data and we insisted that we would continue to evaluate the metric,” said Dan Gavitt, the NCAA’s senior vice president of basketball. “We’ve been very satisfied with its performance thus far, but it became evident after two seasons of use that this change would be an improvement. While we will continue to monitor the metric, I don’t anticipate any additional adjustments for several years. The 2024-25 men's basketball season marks the seventh season of the NCAA Evaluation Tool (NET) rankings, which replaced the RPI prior to the 2018-19 season as the primary sorting tool for evaluating teams.

Now, the key factors influencing a team’s NET ranking are:

Efficiency: The difference between offensive and defensive points per possession.
Strength of Opponents Played: The quality of the teams on their schedule. In addition, the overall and non-conference strength of schedule has been modernized to reflect a truer measure for how hard it is to defeat opponents. The strength of schedule is based on rating every game on a team's schedule for how hard it would be for an NCAA tournament-caliber team to win. It considers opponent strength and site of each game, assigning each game a difficulty score.
Game Location: Where the game was played still matters for quadrant classification.
Wins and Losses: The fundamental outcome of games.
Winner: Who ultimately won the contest.

Since the NET rankings serve as the primary sorting tool for Division I men's basketball, they play an important role in establishing a team's resume.

KenPom: An Independent Voice

Beyond the NCAA’s official metrics, independent analytics Ken Pomeroy offers his highly influential KenPom rankings. It’s the pioneer in this analytical approach.

It’s all about “Adjusted” efficiency. While team efficiency typically measures points scored per 100 possessions, KenPom’s adjustment accounts for the pace of play and the number of possessions a team accumulates per game.

Consider this: Purdue, for example, boasts an exceptional offensive efficiency, projected to score 129 points per 100 possessions. However, their adjusted team efficiency is 65.8 possessions per 40-minute game, ranking them 313th nationally in pace. This means the Boilermakers are highly effective with fewer possessions, outperforming a team like last in the nation, Colorado State (63.0 possessions) even if they run fewer plays.

Key KenPom Metrics

Let’s explore some of KenPom’s key metrics, using Utah State (26th in KenPom, 16th in NET) as our example. Interestingly, KenPom and NET often show discrepancies; for instance, KenPom places San Diego State at 49th, Boise State at 51st, and New Mexico at 54th, while the NET has them at 60th, 44th, and 46th respectively, highlighting the differing methodologies.

Adjusted Efficiency Margin (NetRtg): This is KenPom’s ultimate ranking metric. A higher score indicates a better team. For Utah State, an AEM of 21.17 (26th nationally) suggests they would defeat an average NCAA team by approximately 21 points.
Adjusted Offensive Efficiency (ORtg) and Adjusted Defensive Efficiency (DRtg): These metrics, as discussed, quantify how efficiently a team scores and defends, adjusted for pace. Utah State, for example, has an ORtg of 120.7 (25th) and a DRtg of 99.5 (36th).
Adjusted Tempo (AdjT): This measures the average number of possessions a team has per game, adjusted to a standard 40-minute contest. It’s calculated with the formula: Possessions per game = Field goals attempted - offensive rebounds + turnovers + 0.475 x attempted free throws. This adjustment is crucial for comparing “run-and-gun” teams with slower, grind-it-out squads. Utah State plays at a slightly quicker pace with an AdjT of 69.7, ranking 117th.
Luck Rating: This fascinating metric quantifies a team’s performance in close games. Statistically, teams are expected to win roughly 50% of one-possession games. The luck rating reveals the deviation from this expectation. A positive rating means a team is “luckier” by winning more than their share of tight contests. Utah State’s luck rating of 0.071 (66th) suggests they’ve been fortunate in close finishes. In contrast, 9-5 Wichita State is the unluckiest team (-0.211), while 11-4 Tulane is the luckiest (0.254), meaning the American Conference has both the luckiest and unluckiest teams in the country.
Strength of Schedule (SOS): KenPom’s SOS is based on the average Adjusted Efficiency Margin, Offensive Efficiency, and Defensive Efficiency of a team’s opponents. A higher SOS indicates a tougher slate of games. Utah State’s opponents, for instance, have an average AEM of 2.07, signifying the 127th toughest schedule nationally, meaning the Aggies’s opponents typically win their games by an average of 2 points.
Non-Conference Strength of Schedule (NCSOS): This metric rewards teams that actively seek out challenging non-conference matchups rather than “cupcake” games. Since conference schedules are largely predetermined, the NCSOS reflects a team’s commitment to building a strong resume. Utah State’s average non-conference schedule (151st with an AEM of 0.61) shows a balanced approach. Compare this to Wyoming’s 307th NCSOS (-3.95 AEM), indicating their non-conference opponents typically lost by 4 points. The range is vast, from Fordham’s easiest NCSOS (-11.68) to Alabama’s hardest (12.45).

Key Differences Between NET and KenPom

While both the NET and KenPom strive to quantify team strength, their methodologies lead to distinct differences:

Game Location: The NET heavily integrates game location (home, neutral, away) into its quadrant system, crucially impacting win/loss value. KenPom, on the other hand, does not factor game location directly into its core efficiency rankings.
Efficiency Calculation: KenPom utilizes adjusted efficiency, accounting for a team’s pace of play and number of possessions per game. The NET focuses on points per 100 possessions regardless of the pace, which can sometimes favor slower teams with fewer, more efficient possessions differently.
Scoring Margin: KenPom does consider scoring margin as part of its efficiency calculations. The NET initially included it but has since removed it as a direct factor, though efficiency still captures some aspects of dominance.
Wins & Losses: KenPom primarily focuses on efficiency and predictive metrics; wins and losses are an outcome, not a direct input for its core ranking calculations. The NET, while incorporating efficiency, still fundamentally uses wins and losses, particularly through its quadrant system, as a direct input for ranking.
Methodology: KenPom relies on a strict, formulaic (predictive) approach to generate its rankings. The NET employs a more dynamic learning algorithm that continually compares expected outcomes against actual results, adjusting its model accordingly.

Wins Above Bubble (WAB)

WAB stands for Wins Above Bubble, and it measures-broadly speaking-the number of wins a team has against its schedule relative to how an average bubble team would fare against that same schedule.

Here’s how it works in practice. Let’s say Team A plays a difficult regular-season schedule, against which we would expect the average bubble team to go 14-17. Team A, however, rips off a rock-solid 20-11 record. Team A, then, would have a 6.0 WAB. Think of it like Wins Above Replacement in baseball, which measures the number of wins a player generates relative to the number of wins a hypothetical replacement player would generate. Seth Burn, a gambler who runs this blog, is widely credited with introducing WAB in Feb. 2015.

tags: #ncaa #basketball #rpi #rankings #explained