College Basketball’s NET Rankings, Explained | by Giovanni Malloy | Mar, 2023
How data science drives March Madness
If you are a college basketball fan, you are starting to salivate because March Madness is just around the corner. If you are new to the college basketball scene, March Madness is the name for the NCAA tournament crowning the champion of Division I men’s basketball. Whether you are a burgeoning fan or a 50-year veteran spectator, data science is playing a larger role than ever before in how you experience the game. The teams comprising the tournament are chosen in large part by a data-driven algorithm called NET rankings.
As a data scientist or machine learning engineer, it is important to understand how the field can impact different industries, including sports and entertainment. While college basketball is a late adopter of a growing trend, the NET rankings are a prime example of how the way we shape our algorithms can influence outcomes and incentivize behaviors. If you work in sports analytics, understanding NET rankings is an absolute must, but regardless of your industry, the college basketball world has laid out an important case study in using data science to improve their product and grow their revenue.
Quick introduction to March Madness
For those of you who have never heard of March Madness, this blog requires some additional context: March Madness is a 68-team men’s college basketball tournament that runs from mid-March to early April every year. The winner of the tournament is crowned the National Champion. To start the tournament, there are four play-in games called the “First Four”. After these four games, the remaining 64 teams are then divided into four regions of 16 teams ranked 1–16. The champion of each region makes it to the semi-finals called the “Final Four”.
Much of the discussion throughout the season revolves around March Madness. There is broad interest in the tournament, as it is often an excellent excuse to wager money among friends or in Las Vegas. Of the 68 teams that make up the field, 31 are conference champions and 37 receive “at-large” bids [3]. The study and chatter of how these teams will be organized in the tournament is called “bracketology.” Bracketology is more of an art than a science, however. Deciding who receives the “at-large” bids is a topic of constant debate. This is where NET rankings come into play.
Introduction to NET Rankings
Back in 2018, the NCAA first released a new ranking system called the NCAA Evaluation Tool or NET [1]. The ranking system is a collaboration with Google Cloud Professional Services aimed at providing a data-driven indicator of the quality of a given college basketball team. When the rankings were first released, they relied on five different metrics: Team Value Index, Net Efficiency, win percentage, adjusted win percentage, and scoring margin [2]. However, since then, the rankings have been adjusted to include only Team Value Index and Net Efficiency [1].
There is certainly a debate as to whether this is the best system for determining the quality of teams among sports writers and basketball fans. Regardless of the various opinions of NET rankings, it is used as the basis for decision making by the NCAA selection committee to determine which teams receive an “at-large” bid and how to assign rankings within a region (these rankings are called seeds). All of these decisions can affect the outcome of the tournament. Thus, you can start to see how data science underlies the bedrock of March Madness.
Calculating NET Rankings
NET rankings are driven by data science. The NCAA tweeted this graphic in 2018 to explain the metric:
As you can see, the Team Value Index is a function of the game result, opponent, and location. The algorithm to calculate the Team Value Index is not published and therefore a black box, but we know for sure that an important component of Team Value Index is related to opponent quality. The NET rankings subdivide opponent quality into four quadrants aptly named Quad 1, Quad 2, Quad 3, and Quad 4. According to [4], here is how the quadrants are defined:
- Quad 1: “Home games vs. opponents with NET ranking of 1–30, Neutral games vs. opponents with NET ranking of 1–50, Away games vs. opponents with NET ranking of 1–75” [4]
- Quad 2: “Home games vs. opponents with NET ranking of 31–75, Neutral games vs. opponents with NET ranking of 51–100, Away games vs. opponents with NET ranking of 76–135” [4]
- Quad 3: “Home games vs. opponents with NET ranking of 76–160, Neutral games vs. opponents with NET ranking of 101–200, Away games vs. opponents with NET ranking of 135–240” [4]
- Quad 4: “Home games vs. opponents with NET ranking of 161–363, Neutral games vs. opponents with NET ranking of 201–363, Away games vs. opponents with NET ranking of 241–363” [4]
The Quad system inherently captures features of opponent strength and location. Therefore, regardless of the output Team Value Index, the selection committee focuses heavily on Quad 1 wins and Quad 4 losses when assigning “at-large” bids and tournament seeds.
Net Efficiency, on the other hand, is extremely transparent. Net Efficiency is a function of offensive and defensive efficiency [2]. Offensive efficiency is calculated as:
O = PF/(FGA — OREB+TO+.475*FTA)
Where O is offensive efficiency, PF is points for (total points scored), FGA is field goal attempts (number of shots), OREB is offensive rebounds, TO is turnovers, and FTA is free throw attempts [2].
Defensive efficiency is calculated as:
D = PA/(Opp_FGA — Opp_OREB+Opp_TO+.475*Opp_FTA)
Where D is defensive efficiency, PA is points against, Opp_FGA is opponent’s field goal attempts, Opp_OREB is opponent’s offensive rebounds, Opp_TO is opponent’s turnovers, and Opp_FTA is opponent’s free throw attempts [2].
Net efficiency is simply the difference between offensive and defensive efficiency, or NE = O — D [2]. Net efficiency is a dense metric and captures a team’s performance relative to their opponent in a wholistic manner.
Example NET Team Sheet
So, how does this all come together for the NCAA selection committee? The answer is not completely clear. Obviously, they will have access to the NET rankings. In addition, they will have access to a report on each team in the form of a NET sheet. Each team’s NET sheet is split up into several sections. Across the top of the sheet, there is the NET rank, information on the team record, strength of schedule, opponent average NET rankings, other result-based and predictive rankings, and the win-loss record broken down by opponent quadrant and game location. The bottom half of the sheet is a game-by-game breakdown of team performance divided into sections by opponent NET ranking/quadrant. I suggest you take a look at an example here [5].
While it isn’t the most beautiful data visualization ever created, there is a lot of information packed into a tight space. On the team sheets, the Quad 1and Quad 2games are further divided into upper and lower halves. Also notice that non-conference games are highlighted in blue and delineated in the metrics above. Losses are highlighted in red so as to easily point out bad (Quad 4) losses or great (Quad 1) wins. As you can tell, the quadrant system plays a key role in the presentation of the data.
Limitations of NET Rankings
I know there are many basketball fans out there who are critical of the NET ranking system. No model is perfect, and NET is no exception. However, I will try to highlight some limitations that I see of the NET ranking system from the perspective of a data scientist (in conjunction with a college basketball fan).
The biggest limitation I see with the NET ranking system is that does not take recency into account [1]. While it’s true that consistency over an entire season is valuable and laudable, there is something to be said for peaking at the right time in the season. Whether it is conditioning, chemistry, or confidence, everything needs to align perfectly to have success during March Madness. In basketball speak, these are the “intangibles”. They are not easily measured (although some have tried), but they are changing over time and do affect outcomes. In econometrician speak, this is the “heterogeneity” inherent to the model.
Another curiosity of the NET rankings that I will categorize as a limitation is the delay in their initial release. The NET rankings are updated daily but not until early December — after most teams have played between 5 and 10 games. I think this likely signifies that there is a highly uncertain initialization state for the NET rankings. It would be interesting to know whether each team begins the season in a specific ranking or quadrant based on historical data, subjective intuition, or a random distribution. If we were able to see the initial state of the NET rankings before the first tip-off of the season, I think we could gain some very valuable insight into how the algorithm works. Is it completely naive or is there an element of transfer learning from seasons prior or other polls’ preseason rankings?
To tabulate a final NET ranking, I assume that there is some manner in which Team Value Index is converted to a numerical value and it is combined with Net Efficiency to calculate a weighted metric of team quality. I will admit that NET rankings could very well be a heuristic or other non-AI algorithm. Certainly, the manner in which the Net Efficiency statistic is calculated would suggest that the NCAA would be open to a heuristic-type approach. Moreover, my experience with providing data science insight into a non-technical realm, such as health policy, has shown me that sometimes less is more. More understandable models can sometimes be more attractive to decision makers.
Nonetheless, my third and final limitation relies on the assumption that this is a supervised learning algorithm. If NET rankings are derivative of a supervised learning algorithm, then I wonder where the training data might come from. What would be the baseline truth? How is accuracy measured? What truly distinguishes team #232 from team #233? Even when comparing the same team to itself year over year, you could be comparing wildly different rosters. It would be hard to find meaning in an error metric like root mean squared error.
Hypothesizing the underlying algorithm
So, how does the NET ranking system come together? Perhaps we should try to re-create it? We do know a couple of things for certain:
- The former gold standard statistical model for college basketball rankings, the RPI ranking system, was an elegant but simple heuristic algorithm. Institutions like the NCAA are not necessarily known for innovation, and I doubt the college basketball community wants to feel that its crown jewel tournament is driven by non-interpretable AI algorithm. So, my best guess is that there is limited, if any, machine learning at play. Harkening back to the third limitation I mentioned earlier, a supervised learning approach is probably more trouble than it is worth.
- The NET rankings are in some sense recursive. The NET ranking of a team is dependent on the NET rankings of its opponents which are dependent on the NET rankings of its opponents, and so on and so forth. NET rankings could be driven by a Bayesian approach whereby there is an initial naive distribution assumed for each team, and after each game, that distribution is updated.
- Google Cloud Professional Services are involved. This might be a great example of cognitive bias or clever marketing, but I want to believe that whatever Google touches uses cutting-edge methodology. While not necessarily true, partnering with Google gives the NCAA access to massive computational resources and ability to develop methods beyond the traditional sports analytics’ realm. Even if the algorithm is interpretable, perhaps the structure is complex and potentially even counterintuitve.
- The historical NET rankings are difficult to find. After about an hour of searching the web, it was hard to find any sources that publish the NET rankings each day. This makes me skeptical enough to directly contradict my supposition in point 3. Perhaps, the algorithm is simple enough, that it could be easily re-engineered with access to a season’s worth of data and NET rankings. Perhaps, we could fit a simple linear regression to produce a score value for each team and the NET rankings are a sorted list of the resulting scores.
Given that there are many possible underlying methods to producing the ultimate NET rankings, I believe the most likely scenario is that the NCAA is using ensemble learning, such as voting. This means that they could be taking multiple approaches to producing a NET ranking as a function of Team Value Index and Net Efficiency. Then, they combine the results of these methods to develop a final NET ranking that gets published each week.
Follow the Money
While it is a fun exercise to attempt to surmise the possible methods to produce the NET ranking, my opinion is that the results are not the most important outcome of NET ranking. Of course, the NCAA wants to identify the best teams to make March Madness as competitive and meritorious as possible. So, the rankings need to appear to be reasonable and likely will align closely with the AP and Coaches Polls.
However, the NET rankings do something important for the business of college basketball that no other ranking system explicitly does: NET rankings incentivize high quality non-conference games.
This point cannot be understated. This is the other side of the power of data. By publishing the components of the NET rankings, the NCAA is proclaiming very publicly the metrics to which all teams ought to perform. From a business strategy perspective, the Team Value Index is genius. Like the Net Efficiency metric, it strives to quantify the quality of a college basketball team in an understandable way. Unlike the Net Efficiency metric, however, Team Value Index pushes athletic directors, coaches, and all parties involved in scheduling to ensure that the non-conference slate of games is flush with Quad 1 games. Compared to the conference schedule, non-conference schedules are flexible and versatile. While the importance of non-conference competition has been increasing even before the NET rankings debuted, the Team Value Index and emphasis on the quadrant system in NET rankings formalizes this importance and rewards teams with tougher schedules.
In a sports media and entertainment landscape where streaming is becoming increasingly important, content is king. The greater number of high quality (Quad 1) games that NCAA basketball can produce during the season, the greater the value of the media content that is college basketball. As more streaming services dip their toes into college athletics, improved in-season games means more fans for the sport. More fans mean more intrigue in during both the regular season and post-season. More intrigue means more revenue all season long and much more revenue for the already lucrative March Madness.
If you doubt that NET rankings are intended to incentivize higher quality non-conference play, please refer back to the image of the NET team sheet. The non-conference strength of schedule and records get their own lines at the top while the non-conference games are highlighted in bright cyan. I cannot guarantee that this is the version of the team sheet that the selection committee sees, but it nonetheless supports my claim.
Conclusions
If you are like me, by the end of this blog, you might have more questions than answers. The components of the NET ranking are so simple, but the algorithm that brings those components together into one coherent ranking is shrouded in secrecy. There are many possible methods and models that could be used to produce the NET rankings, but whatever the method is, it is backed by data.
Data is driving March Madness from both ends. The NET rankings inform the selection committee on how to structure the tournament while they explicitly incentivize the teams to improve their non-conference strength of schedule. Overall, I think the NET rankings are good for the sport of basketball and the tournament. They can help reduce bias in selecting and seeding teams during March Madness and improve the quality of the sport during the regular season. So, whether you have never missed a First Four or never heard of the Final Four, now you know how data science is behind it all.
References
[1] College basketball dictionary: 51 terms defined | NCAA.com
[2] College basketball’s NET rankings, explained | NCAA.com
[3] The First Four of the NCAA tournament, explained | NCAA.com
[5] NET — Nitty Gritty Report with Team Sheets for NCAA Men’s College Basketball | WarrenNolan.com
How data science drives March Madness
If you are a college basketball fan, you are starting to salivate because March Madness is just around the corner. If you are new to the college basketball scene, March Madness is the name for the NCAA tournament crowning the champion of Division I men’s basketball. Whether you are a burgeoning fan or a 50-year veteran spectator, data science is playing a larger role than ever before in how you experience the game. The teams comprising the tournament are chosen in large part by a data-driven algorithm called NET rankings.
As a data scientist or machine learning engineer, it is important to understand how the field can impact different industries, including sports and entertainment. While college basketball is a late adopter of a growing trend, the NET rankings are a prime example of how the way we shape our algorithms can influence outcomes and incentivize behaviors. If you work in sports analytics, understanding NET rankings is an absolute must, but regardless of your industry, the college basketball world has laid out an important case study in using data science to improve their product and grow their revenue.
Quick introduction to March Madness
For those of you who have never heard of March Madness, this blog requires some additional context: March Madness is a 68-team men’s college basketball tournament that runs from mid-March to early April every year. The winner of the tournament is crowned the National Champion. To start the tournament, there are four play-in games called the “First Four”. After these four games, the remaining 64 teams are then divided into four regions of 16 teams ranked 1–16. The champion of each region makes it to the semi-finals called the “Final Four”.
Much of the discussion throughout the season revolves around March Madness. There is broad interest in the tournament, as it is often an excellent excuse to wager money among friends or in Las Vegas. Of the 68 teams that make up the field, 31 are conference champions and 37 receive “at-large” bids [3]. The study and chatter of how these teams will be organized in the tournament is called “bracketology.” Bracketology is more of an art than a science, however. Deciding who receives the “at-large” bids is a topic of constant debate. This is where NET rankings come into play.
Introduction to NET Rankings
Back in 2018, the NCAA first released a new ranking system called the NCAA Evaluation Tool or NET [1]. The ranking system is a collaboration with Google Cloud Professional Services aimed at providing a data-driven indicator of the quality of a given college basketball team. When the rankings were first released, they relied on five different metrics: Team Value Index, Net Efficiency, win percentage, adjusted win percentage, and scoring margin [2]. However, since then, the rankings have been adjusted to include only Team Value Index and Net Efficiency [1].
There is certainly a debate as to whether this is the best system for determining the quality of teams among sports writers and basketball fans. Regardless of the various opinions of NET rankings, it is used as the basis for decision making by the NCAA selection committee to determine which teams receive an “at-large” bid and how to assign rankings within a region (these rankings are called seeds). All of these decisions can affect the outcome of the tournament. Thus, you can start to see how data science underlies the bedrock of March Madness.
Calculating NET Rankings
NET rankings are driven by data science. The NCAA tweeted this graphic in 2018 to explain the metric:
As you can see, the Team Value Index is a function of the game result, opponent, and location. The algorithm to calculate the Team Value Index is not published and therefore a black box, but we know for sure that an important component of Team Value Index is related to opponent quality. The NET rankings subdivide opponent quality into four quadrants aptly named Quad 1, Quad 2, Quad 3, and Quad 4. According to [4], here is how the quadrants are defined:
- Quad 1: “Home games vs. opponents with NET ranking of 1–30, Neutral games vs. opponents with NET ranking of 1–50, Away games vs. opponents with NET ranking of 1–75” [4]
- Quad 2: “Home games vs. opponents with NET ranking of 31–75, Neutral games vs. opponents with NET ranking of 51–100, Away games vs. opponents with NET ranking of 76–135” [4]
- Quad 3: “Home games vs. opponents with NET ranking of 76–160, Neutral games vs. opponents with NET ranking of 101–200, Away games vs. opponents with NET ranking of 135–240” [4]
- Quad 4: “Home games vs. opponents with NET ranking of 161–363, Neutral games vs. opponents with NET ranking of 201–363, Away games vs. opponents with NET ranking of 241–363” [4]
The Quad system inherently captures features of opponent strength and location. Therefore, regardless of the output Team Value Index, the selection committee focuses heavily on Quad 1 wins and Quad 4 losses when assigning “at-large” bids and tournament seeds.
Net Efficiency, on the other hand, is extremely transparent. Net Efficiency is a function of offensive and defensive efficiency [2]. Offensive efficiency is calculated as:
O = PF/(FGA — OREB+TO+.475*FTA)
Where O is offensive efficiency, PF is points for (total points scored), FGA is field goal attempts (number of shots), OREB is offensive rebounds, TO is turnovers, and FTA is free throw attempts [2].
Defensive efficiency is calculated as:
D = PA/(Opp_FGA — Opp_OREB+Opp_TO+.475*Opp_FTA)
Where D is defensive efficiency, PA is points against, Opp_FGA is opponent’s field goal attempts, Opp_OREB is opponent’s offensive rebounds, Opp_TO is opponent’s turnovers, and Opp_FTA is opponent’s free throw attempts [2].
Net efficiency is simply the difference between offensive and defensive efficiency, or NE = O — D [2]. Net efficiency is a dense metric and captures a team’s performance relative to their opponent in a wholistic manner.
Example NET Team Sheet
So, how does this all come together for the NCAA selection committee? The answer is not completely clear. Obviously, they will have access to the NET rankings. In addition, they will have access to a report on each team in the form of a NET sheet. Each team’s NET sheet is split up into several sections. Across the top of the sheet, there is the NET rank, information on the team record, strength of schedule, opponent average NET rankings, other result-based and predictive rankings, and the win-loss record broken down by opponent quadrant and game location. The bottom half of the sheet is a game-by-game breakdown of team performance divided into sections by opponent NET ranking/quadrant. I suggest you take a look at an example here [5].
While it isn’t the most beautiful data visualization ever created, there is a lot of information packed into a tight space. On the team sheets, the Quad 1and Quad 2games are further divided into upper and lower halves. Also notice that non-conference games are highlighted in blue and delineated in the metrics above. Losses are highlighted in red so as to easily point out bad (Quad 4) losses or great (Quad 1) wins. As you can tell, the quadrant system plays a key role in the presentation of the data.
Limitations of NET Rankings
I know there are many basketball fans out there who are critical of the NET ranking system. No model is perfect, and NET is no exception. However, I will try to highlight some limitations that I see of the NET ranking system from the perspective of a data scientist (in conjunction with a college basketball fan).
The biggest limitation I see with the NET ranking system is that does not take recency into account [1]. While it’s true that consistency over an entire season is valuable and laudable, there is something to be said for peaking at the right time in the season. Whether it is conditioning, chemistry, or confidence, everything needs to align perfectly to have success during March Madness. In basketball speak, these are the “intangibles”. They are not easily measured (although some have tried), but they are changing over time and do affect outcomes. In econometrician speak, this is the “heterogeneity” inherent to the model.
Another curiosity of the NET rankings that I will categorize as a limitation is the delay in their initial release. The NET rankings are updated daily but not until early December — after most teams have played between 5 and 10 games. I think this likely signifies that there is a highly uncertain initialization state for the NET rankings. It would be interesting to know whether each team begins the season in a specific ranking or quadrant based on historical data, subjective intuition, or a random distribution. If we were able to see the initial state of the NET rankings before the first tip-off of the season, I think we could gain some very valuable insight into how the algorithm works. Is it completely naive or is there an element of transfer learning from seasons prior or other polls’ preseason rankings?
To tabulate a final NET ranking, I assume that there is some manner in which Team Value Index is converted to a numerical value and it is combined with Net Efficiency to calculate a weighted metric of team quality. I will admit that NET rankings could very well be a heuristic or other non-AI algorithm. Certainly, the manner in which the Net Efficiency statistic is calculated would suggest that the NCAA would be open to a heuristic-type approach. Moreover, my experience with providing data science insight into a non-technical realm, such as health policy, has shown me that sometimes less is more. More understandable models can sometimes be more attractive to decision makers.
Nonetheless, my third and final limitation relies on the assumption that this is a supervised learning algorithm. If NET rankings are derivative of a supervised learning algorithm, then I wonder where the training data might come from. What would be the baseline truth? How is accuracy measured? What truly distinguishes team #232 from team #233? Even when comparing the same team to itself year over year, you could be comparing wildly different rosters. It would be hard to find meaning in an error metric like root mean squared error.
Hypothesizing the underlying algorithm
So, how does the NET ranking system come together? Perhaps we should try to re-create it? We do know a couple of things for certain:
- The former gold standard statistical model for college basketball rankings, the RPI ranking system, was an elegant but simple heuristic algorithm. Institutions like the NCAA are not necessarily known for innovation, and I doubt the college basketball community wants to feel that its crown jewel tournament is driven by non-interpretable AI algorithm. So, my best guess is that there is limited, if any, machine learning at play. Harkening back to the third limitation I mentioned earlier, a supervised learning approach is probably more trouble than it is worth.
- The NET rankings are in some sense recursive. The NET ranking of a team is dependent on the NET rankings of its opponents which are dependent on the NET rankings of its opponents, and so on and so forth. NET rankings could be driven by a Bayesian approach whereby there is an initial naive distribution assumed for each team, and after each game, that distribution is updated.
- Google Cloud Professional Services are involved. This might be a great example of cognitive bias or clever marketing, but I want to believe that whatever Google touches uses cutting-edge methodology. While not necessarily true, partnering with Google gives the NCAA access to massive computational resources and ability to develop methods beyond the traditional sports analytics’ realm. Even if the algorithm is interpretable, perhaps the structure is complex and potentially even counterintuitve.
- The historical NET rankings are difficult to find. After about an hour of searching the web, it was hard to find any sources that publish the NET rankings each day. This makes me skeptical enough to directly contradict my supposition in point 3. Perhaps, the algorithm is simple enough, that it could be easily re-engineered with access to a season’s worth of data and NET rankings. Perhaps, we could fit a simple linear regression to produce a score value for each team and the NET rankings are a sorted list of the resulting scores.
Given that there are many possible underlying methods to producing the ultimate NET rankings, I believe the most likely scenario is that the NCAA is using ensemble learning, such as voting. This means that they could be taking multiple approaches to producing a NET ranking as a function of Team Value Index and Net Efficiency. Then, they combine the results of these methods to develop a final NET ranking that gets published each week.
Follow the Money
While it is a fun exercise to attempt to surmise the possible methods to produce the NET ranking, my opinion is that the results are not the most important outcome of NET ranking. Of course, the NCAA wants to identify the best teams to make March Madness as competitive and meritorious as possible. So, the rankings need to appear to be reasonable and likely will align closely with the AP and Coaches Polls.
However, the NET rankings do something important for the business of college basketball that no other ranking system explicitly does: NET rankings incentivize high quality non-conference games.
This point cannot be understated. This is the other side of the power of data. By publishing the components of the NET rankings, the NCAA is proclaiming very publicly the metrics to which all teams ought to perform. From a business strategy perspective, the Team Value Index is genius. Like the Net Efficiency metric, it strives to quantify the quality of a college basketball team in an understandable way. Unlike the Net Efficiency metric, however, Team Value Index pushes athletic directors, coaches, and all parties involved in scheduling to ensure that the non-conference slate of games is flush with Quad 1 games. Compared to the conference schedule, non-conference schedules are flexible and versatile. While the importance of non-conference competition has been increasing even before the NET rankings debuted, the Team Value Index and emphasis on the quadrant system in NET rankings formalizes this importance and rewards teams with tougher schedules.
In a sports media and entertainment landscape where streaming is becoming increasingly important, content is king. The greater number of high quality (Quad 1) games that NCAA basketball can produce during the season, the greater the value of the media content that is college basketball. As more streaming services dip their toes into college athletics, improved in-season games means more fans for the sport. More fans mean more intrigue in during both the regular season and post-season. More intrigue means more revenue all season long and much more revenue for the already lucrative March Madness.
If you doubt that NET rankings are intended to incentivize higher quality non-conference play, please refer back to the image of the NET team sheet. The non-conference strength of schedule and records get their own lines at the top while the non-conference games are highlighted in bright cyan. I cannot guarantee that this is the version of the team sheet that the selection committee sees, but it nonetheless supports my claim.
Conclusions
If you are like me, by the end of this blog, you might have more questions than answers. The components of the NET ranking are so simple, but the algorithm that brings those components together into one coherent ranking is shrouded in secrecy. There are many possible methods and models that could be used to produce the NET rankings, but whatever the method is, it is backed by data.
Data is driving March Madness from both ends. The NET rankings inform the selection committee on how to structure the tournament while they explicitly incentivize the teams to improve their non-conference strength of schedule. Overall, I think the NET rankings are good for the sport of basketball and the tournament. They can help reduce bias in selecting and seeding teams during March Madness and improve the quality of the sport during the regular season. So, whether you have never missed a First Four or never heard of the Final Four, now you know how data science is behind it all.
References
[1] College basketball dictionary: 51 terms defined | NCAA.com
[2] College basketball’s NET rankings, explained | NCAA.com
[3] The First Four of the NCAA tournament, explained | NCAA.com
[5] NET — Nitty Gritty Report with Team Sheets for NCAA Men’s College Basketball | WarrenNolan.com
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