This project predicts the All-Star lineup this year using results from Phase 1 and common stats from previous years (games, age, runs, hits, home runs, RBIs, strikeouts, batting average) that were predictive of whether one was an All Star in the past. Using data from Baseball Reference, we compiled data from 2017-2019 of every player’s stats and added data of whether or not they had been an All-Star in that given year.  We imported the data into an analytics software tool called Jump (JMP) and, using JMP’s partition function, we analyzed how predictive certain stats were in determining All-Star status.

This is just part of the entire partition but for instance, as shown above, a total of 61 players from 2017-2019 had over 115 hits and over 100 runs during a given year, with 23 of them being voted by fans as All-Star starters during that year. Thus, having over 115 hits and over 100 runs is quite predictive in determining whether one would be an All-Star. However, having over 115 hits and over 100 runs but with fewer than 37 home runs and 79 RBIs is not predictive of being an All-Star, and therefore players with those stats from 2021 (all these metrics were divided by two when we carried them over to 2021 since we are only halfway through the season) are eliminated from our list of possible All-Star predictions. In this way, we used this partition tree and its given stat metrics and filtered through the large data set of players from 2021 and eliminated players that failed to meet certain criteria of the partition. In the end, we had 63 players on our list that had passed through the partition and 25 of them ended up being part of the 51 finalists for the 2021 All Star game.

A small flaw with the partition was that it failed to account for injuries as several star names did not make it onto our list. For instance, Mike Trout, who is 4th in the league with votes (sitting at just over 2 million) failed to make it far in the partition as he only played 36 games and failed to acquire the 58 hits needed (115 hits/2) in the partition criteria. Likewise Bryce Harper, who has been forced to miss over 20 games due to injuries, also failed to rack up the number of hits needed to pass through the partition. The partition also only focused strictly on the player’s performance this year and did not account for how that player had done in previous years. For example, take Juan Soto. Juan Soto, who is notoriously known for his bold demeanor and for carrying the Nationals to their World Series victory in 2019, has been significantly underperforming this year in comparison to his incredible 2019 season. Under the partition criteria, Soto is lacking in runs, batting average, and RBIs while in 2019 he surpassed every statistic easily. In fact, the outfield players that successfully passed through the partition (ex. Raimel Taipas, Teoscar Hernandez, Bryan Reynolds) significantly surpass Soto in every major statistic despite only playing a few more games than him. Yet many of these players are nowhere close to having as many votes or as much popularity in the baseball world as Soto. Thus, this example shows the emphasis that the public places on past performance and/or popularity rather than on performance from the present season in their voting decisions.

Using the results of our partition and the results from phase 1, here is our prediction for the 2021 All Star starting lineup:

NL:

• Catcher: Buster Posey**
• 1B: Freddie Freeman
• 2B: Adam Frazier*
• SS: Fernando Tatis Jr.
• 3B: Kris Bryant
• OF: Ronald Acuna Jr., Nick Castellanos, Jesse Winker

AL:

• Catcher: Salvador Perez
• 1B: Vladimir Guerrero
• 2B: Marcus Semien
• SS: Xander Bogaerts**
• 3B: Rafael Devers
• OF: Aaron Judge,  Adolis Garcia, Teoscar Hernandez
• DH: Shohei Ohtani

**  None of our predictions for this position and league were correct, but out of the three predetermined finalists, this player made it the farthest through our partition. The number of votes a given player received played no role whatsoever in our predictions.

The post 2021 MLB ALL-STAR PREDICTIONS first appeared on 'CATS STATS.

The surfaces the matches are played on tend to affect the way the players compete, last on court and react to certain shots. Tennis being a sport where the matches are not timed, some players take a considerable amount of time with their activities on the court and others move quickly. In this post, the main purpose is to compare the standard deviation of the total points, the average of total points played, and average of the time played.  From this, we compute the standard deviation of that time played in the major grand slams played in 2017.  While the tennis surfaces differ, how does this side of play differ in grand slam matches in men’s tennis?

First, we estimate the number of second per point as the total seconds for a match divided by the number of points. Note, this is an estimate since the total time of a match includes rests. Professional tennis includes a 90-second rest between end changes, which is extended to two minutes at the end of a set, although the first changeover of the next set does not include a rest. Future work could subtract such rests from the overall time.

Grass is often noted as a fast surface for tennis.  The data supports this.  Wimbledon’s matches averaged 37 seconds per point with a smaller standard deviation of 4. Matches averaged 192 total points with a standard deviation of about 72.  The Australian Open had longer points, averaging 39 seconds per point with a standard deviation of approximately 6 seconds. In all, matches were slightly shorter, averaging 189 points with a standard deviation of 68.  The next longest was the US Open averaging 40 points per second with a standard deviation of 4.  The average number of points was longer at 221 points and a standard deviation of 61.  Finally, clay was the slowest surface in the calculations.  The French Open averaged 41 seconds per point with a standard deviation of 4.  Matches averaged 181 points with a standard deviation of 66. 

The order of points played from highest to lowest are the US Open, Wimbledon, the French Open, and the Australian open. In terms of the calculated average seconds per point, the order from highest to lowest are the French Open, US Open, Australian Open and Wimbledon. While the differences per point are small, they accumulate over time and also underscore the impact of the court surfaces. The French Open had the highest average seconds per point of the grand slam tournaments. Clay courts are notoriously slow, and the rallies take time to be exchanged.

A lot of factors can lead to these differences in average seconds per point and total points in a match.  Tennis is quite hard to predict, as it is not timed and some points and length of the points can be affected by how technically and emotionally sound the players are. Regardless, sit back and enjoy 2021 Wimbledon and feel free to measure the length of points to see how close to the average your match falls. 

The post What’s the time? Time and total point differences in grand slam tennis first appeared on 'CATS STATS.

But perhaps the largest storyline of the tournament so far has been the Big 10. With 9 teams originally qualifying, per 538’s data, the Big 10 had a 0.158% chance of not having any teams reach the Sweet 16. Just prior to the Michigan game, that same probability had risen to 25.6% with 8 Big 10 teams eliminated. Michigan is now the only Big 10 team left standing.

#1 Illinois vs. #8 Loyola Chicago

The first major upset of the Second Round happened to happen in the first game, with Loyola Chicago a 13-point loss. Going into the game, the probability model I use gave Illinois a 64.66% chance of winning, with an estimated point margin of 4.14. With the loss, Illinois became the 21^st 1 seed to not make the Sweet 16. Whatever questions existed about how Loyola Chicago’s defense would hold up against more athletic teams than they typically see in the Missouri Valley Conference have surely been erased, as Illinois was held to under 60 points for the first time this season. Loyola’s defense held Illinois to 28.6% 3pt. shooting (9% below Illinois’s season average) and created 17 turnovers (4 more than Illinois’s season average). All this was done while giving 13 total free throws to Illinois, compared to the 22 total Loyola Chicago received.

With the result, Loyola Chicago and Illinois metrics-based chances of making the Final Four essentially flipped. Loyola Chicago’s chances soared from 10.69% to 30.27%: the largest total gain in the tournament up until this point. Unsurprisingly, Houston’s chances of making the Final Four went up ~5% as well, largely due to the best metrics-based team in the region now out of contention.

Interestingly, this switch reflects a trend I first learned about in this video from Secret Base. Before the tournament kicked off, I looked at the calculated average win percentage by seed in the Sweet 16. The 6-10 seeds all have an easier path if they manage to get past the winner of the 1 vs. 16 game in the Second Round. This has certainly held true for Loyola Chicago, whose chances of reaching the Final 4/winning the National Championship originally faced a roadblock in Illinois.

This is certainly applicable in Loyola Chicago’s case, who now faces 12 seed Oregon State in their next matchup. The same model gives Loyola Chicago a 73.5% chance of winning, with an estimated point margin of 7.

#3 West Virginia vs. #11 Syracuse

The Midwest Region led the other regions in total number of upsets during the Round of 32 (with 3), and the second of the day happened when Syracuse beat West Virginia 75-72. This caused a similar pattern that occurred with the Loyola Chicago game to occur, except with Syracuse overtakes West Virginia’s previous chances of reaching the Final 4 after the results of this game were factored in.

#7 Florida vs. #15 Oral Roberts

I am pretty sure it is safe to say that Oral Roberts is the biggest Cinderella story in a tournament with many Cinderellas. Coming off their upset win over #2 Ohio State, they beat #7 Florida 81-78, improving their chances at winning the championship another 487% over where they were prior to the Round of 32. Their chances of winning have now gone from 0.000168% at the start of the tournament to 0.00644% at the end of the second round. For a visual representation of just how much the Florida win impacted their chances compared to the first round, check out this graph:

#4 Oklahoma St. vs. #12 Oregon St.

If anything, the PAC-12 has surprised many in just how underrated some of its teams are, while the Big 10 and Big 12—despite the number of teams admitted from each conference—have been largely underwhelming. This matchup between Oklahoma State and Oregon State was another example of this. I will point out that Oklahoma State sits at 33^rd in the KenPom ratings at time of writing, with Oregon State not sitting too far behind at 48^th, so the seed differential of 8 between the two teams is a bit deceiving. Nonetheless, the PAC-12 tournament champions Oregon State came out with a 10-point win over the Big 12 runner-up. Oregon State held Oklahoma State to under 30% shooting (both overall and 3pt), 19% below their season average FG% and 7% below their season average 3PT%. With potential tough matchups against Loyola Chicago and the winner of Syracuse/Houston facing them, the Beavers sit with about a 6% chance of making the Final 4, up 4.3% total since the start of the round.

And that’s just Day One.

#2 Iowa vs. #7 Oregon

This one shocked a lot of people. In what was essentially an offensive shootout, Oregon took control of the game toward the end of the first half and never looked back in yet another example of the PAC-12 outperforming projections while the Big 10 and Big 12 underperform (I’ll be doing a deeper analysis of this at some point, I’m sure). Oregon was a pretty sizable underdog coming into this game, and the 8% total jump in their chances of making the Final 4 shows that (about a 261% jump).

I think the most interesting thing about this game wasn’t that Iowa necessarily played badly—they pretty much were a touch below their season averages in most statistical categories. Rather Oregon’s offense was able to catch fire, allowing Oregon to put up 95 points (20 above their season average, and with ~10% better than average FG%) and the Iowa defense was unable to stop it.

#3 Kansas vs. #6 USC

A lot of people had doubts about USC and their potential to make a deep run. USC seems to just keep proving those doubts wrong, putting up a 34-point win over the #3 seed. In yet another example of the PAC-12 beating lower-seeded Big 10 and Big 12 teams, USC managed to hold Kansas to their lowest score of the season, and their third worst loss in program history. USC’s chances of making the Final 4 nearly doubled as a result of the game, up to 17.38% from 9.48%. Metrics-wise, USC was actually favored to win the game by 1.5 points, translating to a win probability of 55% favoring USC.

Notes on Methodology:

These probabilities all assume that metrics in this pandemic season are a measure of ability. They are and they, as we’ve seen, are not. It’s all part of why we watch and what leads to the Madness of March. 

To calculate win probability for an individual match, KenPom’s Adjusted Efficiency Margin and ESPN’s BPI metrics are first used to determine an estimated point margin. Factors such as notable injuries and rest days are taken into account when generating the point margin. This point margin is then applied to a normalized distribution curve with a standard deviation of 11 (what KenPom uses to calculate its win probabilities). To come up with a team’s odds of winning the tournament, weighted averages of win probabilities against all possible teams in each round of the tournament are used. This yields similar results to FiveThirtyEight and other groups predicting tournament/round win probabilities.

The post Round 2: The Madness Continues first appeared on 'CATS STATS.

2^12 yields that for 12 games, there are 4096 possible outcomes. Dividing 14.7 million by 4096 reveals that each outcome should be present ~3589 times, or about 3.45x as many perfect brackets as there were at that point in time. This trend continued to hold for a few games, before human perfect brackets started to outperform the coin toss again. By end of the first day of games, 14.7-million coin-flip brackets would have ~224 that remained perfect, compared to the 108 remaining. When the last brackets were finally busted late on the second day, human-picked brackets had once again become more effective.

Tournament Win Probability Swings:

Through the first 32 games, teams’ chances of winning the tournament have fluctuated quite a bit. Here’s an overview of the largest gains as a result of individual games, both by percent change and total increase:

By % Change:

By Total Increase:

If looking at the largest swings over the entire course of all 32 games:

By % Change:

By Total Change:

If the percent change for each team is graphed, it forms a pretty nice pattern that makes it easy to see where major upsets occurred, along with how impactful an upset was for the rest of the field.

Overall, two games in the first round stand out as particularly impactful even for teams that did not play in them: #13 Ohio’s upset over #4 Virginia and #2 Oral Roberts upset over #2 Ohio State. In the case of the Ohio upset, Creighton (their opponent in the next game) will now no longer have to take on a Virginia team with better metrics (via KenPom and ESPN BPI) than Ohio. Their chances of winning the entire tournament increased .47% total as a result.

Further, Gonzaga, after their chances decreased throughout the day due to a lack of upsets in their region, went up 1.36% total again due to Virginia’s exiting the tournament.

And in the case of the Oral Roberts upset, Florida’s chances changed significantly, up 97%, due to now facing Oral Roberts rather than an Ohio State team with significantly better metrics.

These probabilities all assume that metrics in this pandemic season are a measure of ability. They are and they, as we’ve seen, are not. It’s all part of why we watch and what leads to the Madness of March.

Notes on Methodology:

To calculate win probability for an individual match, KenPom’s Adjusted Efficiency Margin and ESPN’s BPI metrics are first used to determine an estimated point margin. Factors such as notable injuries and rest days are taken into account when generating the point margin. This point margin is then applied to a normalized distribution curve with a standard deviation of 11 (what KenPom uses to calculate its win probabilities). To come up with a team’s odds of winning the tournament, weighted averages of win probabilities against all possible teams in each round of the tournament are used. This yields similar results to FiveThirtyEight and other groups predicting tournament/round win probabilities.

The post Round One Recap first appeared on 'CATS STATS.

COVID-19 has caused many game cancellations and program shutdowns. When analyzing the field of 68, there are some important COVID-19 implications to address. Most teams did not play as many games as they would in an usual season and some teams did not have a consistent roster throughout the season due to COVID-19 outbreaks. Depending on the severity of the outbreak, a team could have completely shut down basketball activities. When analyzing selections for a bracket, the time at which the outbreak occurred is important. Outbreaks at the beginning of the season have less effect on the caliber of a team, as they have returned to normal actives and schedules. Outbreaks towards the end of the season have a higher impact, as teams may have lasting effects such as the deterioration of skills. It is extremely hard to come back from a hiatus and continue to play at the same intensity and skill level, making it very unlikely for it to elevate. Also, with the diminished schedules, it can be crucial to analyze what games were missed during a program’s outbreak. If a program missed a game against a high-quality opponent or a conference tournament game, the team could lose several advantages. The first being a first-hand account of playing a team they could meet in the tournament. Another being not experiencing the intensity and environment of high quality or highly anticipated game. In games like those, coaches have the opportunity to analyze their teams. 

Some other general points of emphasis when considering selections for brackets are defense, stats in the last two minutes of a half, rebounding, and the age of a team. The defense is becoming more important as college basketball is becoming slow-paced. The decreased speed of the game creates a more grinding and physical game. The effect of a slow-paced game is emphasized in the NCAA tournament. The last two minutes of the game are often the most critical points within a game. Throughout the tournament, many games will be won and lost in the last two minutes of a game. Analyzing a team’s ability to perform well in the last two minutes, can give insight on whether they can close out games in the clutch essentially making them viable or unviable. Rebounding is indubitably one of the most important phases of the game. A rebound determines the team that receives possession of the basketball. In a slow, physical, defensive game, each possession decides which team wins the games, and the more possessions a team has, the higher the chances of winning. For NCAA champions, young teams generally do not perform as well as veteran teams in the NCAA tournament. Winning in the NCAA tournament varies from winning the regular season games, as it is more difficult. The most overlooked difficulty is the experience it takes. For all the freshmen across the country, the tournament is an entirely new experience for them. With the quick turnarounds, changing opponents, and game-plans drastically quick, combined with the change to a slower-paced game, a freshman can be thrown completely array. It is crucial to have experienced players to contribute to the team, especially if they can contribute tournament experience since it is drastically different from a normal game. Looking at every NCAA champion since 2010, every team has had at least two upperclassmen players, that were included in the top seven minutes played. Although every sports fan indulges in the glamor of a superb freshman, the experience is critical to the success of a team in the NCAA tournament. 

When scouting a team, I, Morgan, gather background information on the team such as getting a good idea of the starting five, or the seven players who consistently rack up minutes. Then using Synergy, I break down and elevate each player along with highlighting how to enhance their weaknesses and conceal their strengths. Then I go and evaluate the film on the software’s film feature and cite examples in my report so the coaches can be aware and have evidence of my claims. After that I compare how opponents played against common opponents, other teams that my team has already played, to show how the techniques I suggested work and will affect the outcome. 

The post Bracket tips from Cats Stats Scouting first appeared on 'CATS STATS.

After finding these results, I observed how many times each conference won the championship over those fourteen years, and the percent that correlates:

The main finding that stood out to me was the fact that the Big Ten won nearly 30% of March Madness tournaments when neither Duke nor Kentucky were in the tournament. This year, the big ten has eight teams in the tournament:

Given that both Duke and Kentucky are out of the tournament, and based on previous trends and this year’s rankings, should those making March Madness brackets favor a Big Ten team to win it all?

The post With Kentucky and Duke out, who is left to favor? first appeared on 'CATS STATS.

One of the most important games to be cancelled during the season was Gonzaga against Baylor on December 5th, 2020. The two schools are the top two overall seeds in this year’s March Madness tournament, and a regular season matchup would have not only provided insight on a true favorite in the tournament, but also could have altered the seedings. 

To examine the potential effects of this cancelled game, we analyzed various possible outcomes and the resulting changes in Colley and Massey rankings. Entering March Madness, Gonzaga is ranked 1st in both the Colley and Massey rankings, while Baylor is ranked 2nd in Colley and 4th in Massey. Using the Colley rankings, a Baylor win in the cancelled game would have not changed the rankings but would have cut the difference in Colley rating between the two teams in half. A Gonzaga win, however, would have kept them atop the rankings while dropping Baylor to 3rd. Since Massey rankings depend on point differential, we analyzed close games and blowouts. Here are the results:

Had this game been played, Gonzaga’s seeding as the top seed would have likely not changed regardless of the outcome. Baylor, however, could have potentially moved up a spot, but a bad loss would have dropped them out of the top four in the rankings.

In addition to cancelled games, some conferences elected for their teams to only play conference games. Patriot League teams only played within the conference, and conference champion Colgate had a very strong season, going 11-1 in the regular season and winning all three games in the tournament to clinch an automatic bid to the NCAA Tournament. Colgate’s nearly perfect record places them 14th in the Colley rankings and 9th in the Massey rankings. The Raiders, however, are a #14 seed in March Madness, likely as a result of their lack of non-conference games. How different could their rankings and seeding look if they played a non-conference schedule? We analyzed possible non-conference games for Colgate to try and answer this question.

One usual non-conference opponent for Colgate is fellow tournament team Syracuse. The two New York schools had played every season since 2005 until this year. Syracuse sits 45th in the Colley rankings and 40th in the Massey rankings. If the two teams had played this season, a Colgate win would have moved them up to 10th in the Colley rankings while a Syracuse win would have dropped them to 28th. For the Massey effects, we analyzed the same scenarios as before:

Playing Syracuse would not have been significantly beneficial for Colgate, as even a blowout win would have only moved them up two spots, but a large loss could have sent them much farther down the order in both ranking systems.

Another potential non-conference matchup for Colgate this season would have been Clemson, another tournament team. The two teams faced off last season during non-conference play. The Tigers rank 22nd in the Colley rankings and 43rd in the Massey rankings. Using the same process as before with Syracuse, a Colgate win would have bumped them up to 9th in the Colley rankings, and a loss to Clemson would have moved them down to 26th. The Massey changes are as follows:

Once again, Colgate did not stand to gain too many places by beating Clemson, but a bad loss would have damaged both of their rankings. The small possible gains and large possible drops in the rankings suggest that Colgate may have been better off not having non-conference games, but the Tournament Selection Committee does not purely use Colley and Massey rankings. 

So, it’s time for March Madness but like much in our pandemic world, the tournament is likely to be a wild ride.  Enjoy the beauty of your bracket before the tournament starts as bracket busting is likely to come quick this year.  

This article was developed by Noah Baker and Hope Anderson.

The post Maddening Seeds of Doubt first appeared on 'CATS STATS.

The pandemic has caused the overall #1 seed, Gonzaga, to miss seven games. The most important being an early-season matchup against the #1 seed from the South region, Baylor. The implications of the missed game were massive as it was an early matchup of the top two teams. The game was slated as the most anticipated matchup up until that point. The result of missing highly anticipated matchups could be fatal, as wins against ranked opponents hold a higher value than other slated games. The more importance a win holds, the more implications it has on the selection committee when reviewing resumes for tournament play. Michigan, the #1 seed in the East missed six games, all within their conference schedule. One of the missed games was a highly anticipated matchup against the #1 seed from the Midwest and then ranked #4, Illinois. The implications of missing conference games could result in lower seeding in conference tournaments along with losing potentially high-valued conference wins. Winning the conference tournament grants an automatic bid into the NCAA tournament, so seeding is crucial to winning the conference tournament, which is crucial to bracket bidding. 

Michigan played the Fighting Illini later in their schedule, resulting in a loss. The absence of the first game between the two holds plenty of uncertainty. If they met before, would Michigan have evened the series at one apiece, or would the Illini have swept them, increasing their bracket stock, or would the outcome of the game be different if Michigan did not have to delay their games and continued playing at the same extremely high level? The Illinois program did not shut down, but they missed five games. The effects of shutting down an entire program could be detrimental to the performance or morale of a team. A complete program shutdown lasts a week minimum, which could prohibit the development and retention of skills, along with the installation and development of game plans. 

With the overall health of programs around the country looming large from week to week and the big dance approaching, coaches, players, and staff are on high alert. In the past week, there have been examples of a failed protocol as Duke, Virginia, and Kansas all withdrew from their conference tournaments due to a positive test within the program. Among all the protocols for the NCAA Tournament, there are two very important protocols: “each team must have at least 5 eligible players”, and “if teams are impacted by COVID-19 they will be forced to withdraw and a contingency plan will be drawn up to replace the team.” Essentially no matter how large the impact of COVID-19, as long as a team can lineup five players, they can participate in the tournament giving teams some hope and alleviate some fears among programs. With two of the “Blue Blood” schools missing the tournament in Duke and Kentucky, the second protocol observed could be the newest bid to participate. With the possibility of a team testing positive at any moment’s notice, the NCAA has ruled that a team could be replaced by another team that did not qualify for the tournament originally. The usual unfavored, underdog “Cinderella Story” make take a spin this year to a five-player under-manned and underpowered team instead. Or even a replacement team that goes on to shock the world. 

References:

https://www.cbssports.com/college-basketball/teams/GONZAG/gonzaga-bulldogs/schedule/regular/

https://www.cbssports.com/college-basketball/teams/MICH/michigan-wolverines/schedule/regular/

https://www.cbssports.com/college-basketball/teams/ILL/illinois-fighting-illini/schedule/regular/

https://www.cbssports.com/college-basketball/teams/BAYLOR/baylor-bears/schedule/regular/

https://www.si.com/college/2021/03/12/covid-19-protocols-ncaa-basketball-tournament

The post Pandemic Madness first appeared on 'CATS STATS.

Davidson @ George Washington, February 18, 2021

Davidson:

Davidson is 2-6 away from home and is 7-10 overall this season. Their middling 64.4 points per game have earned them the 180 rank in the nation. On the defensive end of the floor, Davidson does fine, giving opposing teams 68.9 points per game. On the season Davidson has scored on 39.4% of their attempts from the field. From beyond the arc they make 121 shots per game at a 30.3% clip. They also shoot 68.6% from the foul line. In victory, Davidson puts up 4.0 more points than their opponents, but in losses they put up 8.6 fewer points.

Chloe Welch leads the scoring for Davidson with 16.2 points per game while shooting 45.1%. Suzi-Rose Deegan also chips in with 10.7 PPG on 42.9% shooting. Cassidy Gould supports Welch and Deegan, adding 8.9 PPG while shooting 42.9% from the field. Katie Turner affects the scoring in a different way, accumulating 3.3 assists per game. Though not a top-3 scorer, Sarah Konstans commands the paint, bringing down 6.0 boards per game. A nuisance for opposing offenses, Deegan’s 1.4 steals per game help Davidson win the ball back. Adelaide Fuller is a force to be reckoned with in the paint, blocking 0.5 shots per game.

George Washington:

At home George Washington has a record of 4-3, and is 5-12 in all competitions. Their offense has been subpar this season, putting in just 55.9 points per game. This ranks 298 in the NCAA. George Washington’s defense has had a great season so far, with opposing teams only scoring 59.1 points per game. On the season George Washington has scored on 37.6% of their attempts from the field. From beyond the arc they make 56 shots per game at a 24.1% clip. They also shoot 67.4% from the foul line. In victory, George Washington puts up 4.6 more points than their opponents, but in losses they put up 7.8 fewer points.

George Washington’s leading scorer is Ali Brigham, who pours in 12.3 points per game on 52.8% shooting. Jasmine Whitney contributes 8.5 PPG while shooting 33.1%. Giving the team 7.9 points and shooting at an 39.7% clip, Neila Luma complements Brigham and Whitney nicely as a third option. Whitney helps control the offense by adding 4.8 assists per game. Luma is also a great rebounder, grabbing 5.7 boards per game. A nuisance for opposing offenses, Whitney’s 3.0 steals per game help George Washington win the ball back. Brigham is a force to be reckoned with in the paint, blocking 1.2 shots per game.

The post Davidson-GW WBB Preview first appeared on 'CATS STATS.

Supplying analytics is what Cats Stats does and we have supplied analytics to CBS Sports, ESPN, and Fox Sports. Gabe Levy of Cats Stats did some quick computations and we have our estimate!

Since 2015, Steph began taking a higher rate of 3 pointers per game. He’s been relatively consistent since that time. So, we’ll use Curry’s rate of three-pointers since the 2015-2016 season.

Specifically, Steph Curry has 2,584 career three-point field goals. This is 389 behind the all-time leader Ray Allen, who has 2,973. Before the 2015-2016 season, Curry never averaged more than 8.1 three-point attempts per game. Since then, he has never averaged less than 9.8 attempts. Based on the data since the 2015-16 season, Curry is averaging slightly more than 4.6 three-point makes per game. Now, the Warriors are a different team this year. True. Even so, Curry is averaging 4.5 three-point makes per game this season. If we take his average makes per game since the 2015-2016 season, it will take Curry another 84.499 games to tie the record. Thus, he will break the record in the 85th game from now. Being very specific, our computations have Curry breaking the record in 84.716 games.

So, it could happen this year if the Warriors made it late into the playoffs but is more likely to happen next season.

The post Steph’s Recording Breaking Rate first appeared on 'CATS STATS.