Analytics vs. Heart and determination

[cubuffsdoug]

I know several posters here rely on analytics to justify their take on how Rutgers will perform against the rest of the B1G, but did any of them consider other factors like heart or determination? On paper, Nebraska was a clear favorite over the first two opponents, yet they were defeated in both games. I don't think any expert or Nebraska fan considered the other teams were determined to win and were better prepared. The opponents believed in themselves and had something to prove. Also, something else lost in all of this analytics is actual matchups of teams. Most of the data from analytics are overall views on how a team should perform and not individual game matchups. This is where I differ from a lot of posters on here because I believe preparation and matchups have more impact on the outcome of games. For example, according to analytics, Rutgers should have never been able to play Iowa like they did last year. But upon closer inspection, Rutgers had far more advantages over Iowa than what the analytics claimed. RHJ best exemplified Rutgers' strength when he played with his heart and believed his team was better after a painful loss to Iowa earlier in the season. Hell, Bryant did it to Rutgers this year.

I'm old school when it comes to analytics vs. guts, heart, determination. Sports are not played on paper, but in the mind of the athlete, and it reflects on the field, court, or diamond. I don't have to be the biggest guy or the tallest, but I have to want it more and know how to attack my opponents' weaknesses to break their will. This is why I think this team will be special this year. Eugene gave them a "chip on the shoulder" in the way he left, and throw in how the media/fans of the B1G continue to be doubters/haters will only add fuel to the fire.

 

[kcg88]

Nebraska was a clear favorite but had an entirely new roster so the spreads were kind of a guess with them.

Also re: Iowa, KenPom had RU about 33% to win at home and 15% on the road. To win as big as they did was probably like 3%, but a) that doesn't account for Iowa not having their head coach, and b) just about every team has a game like that. You can't cherry pick.

Of course analytics can't tell us what's going to happen in a given game. It can just probabilisticly predict a range of outcomes.

 

[anon_0k9zlfz6lz9oy]

I believe in analytics but to me it’s something I would use to validate my beliefs rather than mold my beliefs. For example, I think we are going to beat Seton Hall despite them being KenPom 20 while we’re KenPom 69. I just think we match up incredibly well vs them. KenPom predicts a 72-70 SHU win.

theres actually a good big ten podcast with Ken Pomeroy himself last week where he kind of explains his system and the degree of variance that it may have especially early in the season and how he comes up with things like pre-season rankings. Worth a listen https://podcasts.apple.com/us/podcast/btn-take-ten-podcast/id1240980185?i=1000456189030

 

[cubuffsdoug]

Nebraska was a clear favorite but had an entirely new roster so the spreads were kind of a guess with them.

Also re: Iowa, KenPom had RU about 33% to win at home and 15% on the road. To win as big as they did was probably like 3%, but a) that doesn't account for Iowa not having their head coach, and b) just about every team has a game like that. You can't cherry pick.

Of course analytics can't tell us what's going to happen in a given game. It can just probabilisticly predict a range of outcomes.

That's part of my point. If the game was played on paper, why bother to play? I think KenPom and other analytics formula factor in the name of the team as well even though it doesn't show up with the info given.

I wasn't cherry-picking. Analytics is about the average performance by a team and weighs heavy on previous year perform to help determine a baseline for the current year. The season is long, and there will be ups and downs based on the mindset of "kids." Rutgers lost to Fordham and played several close games with lesser opponents. It wasn't because Rutgers was bad but outworked, and the opponent forced the matchup in their favor. The opponents were able to attack Rutgers' weaknesses then. Rutgers, in return, did the same against several B1G opponents. When I consider these factors such as matchups, Rutgers is in a better position this year than years past. Analytics never consider scouting reports, injuries, preparedness, and coaching decisions. You have to take into account which B1G coach can get his team to outperform expectations, and right now, Coach Pike is a little ahead of several mid-tier B1G coaches in that area.

 

[cubuffsdoug]

I believe in analytics but to me it’s something I would use to validate my beliefs rather than mold my beliefs. For example, I think we are going to beat Seton Hall despite them being KenPom 20 while we’re KenPom 69. I just think we match up incredibly well vs them. KenPom predicts a 72-70 SHU win.

theres actually a good big ten podcast with Ken Pomeroy himself last week where he kind of explains his system and the degree of variance that it may have especially early in the season and how he comes up with things like pre-season rankings. Worth a listen https://podcasts.apple.com/us/podcast/btn-take-ten-podcast/id1240980185?i=1000456189030

I agree. I think some people used analytics so much what is the point of having a coach? I see analytics as another tool for a coach to use in developing and preparing his team.

Match up: I don't think analytics are even considered as a factor, but a good coach could use that data as way to swing the match up in his favor. Rutgers match up well this season with plenty of their opponents. More so this year than any other year I can remember.

 

[YoucancallmeRay]

Your original post talks about heart and determination and then mentions matchups. These are different factors, matchups not having to do with heart and determination. If heart and determination leads to better focus and therefore better execution, then you have a point, but you can play with heart and determination and lose just as well if you don't execute. It comes down to playing good defense, not turning the ball over, finding the best shot, and putting the ball in the hole. Execution trumps heart and determination. Talent might play a role too, lol.

 

[Greene Rice FIG]

I believe Russ Wood has interesting data that dives deep in to games and probably has good use for coaches. The data that most of us spit out (on message board) is really an explanation of how players perform and guesses how teams will do against each other. Very little of the data has any use for coaching staffs or scout teams.

kenpom and bartovik will tell you a team is not good defensively, but it won't tell you because they don't defend screens well. It won't tell you what side of the floor to attack or what player you should use to set the screen.

 

[cubuffsdoug]

Your original post talks about heart and determination and then mentions matchups. These are different factors, matchups not having to do with heart and determination. If heart and determination leads to better focus and therefore better execution, then you have a point, but you can play with heart and determination and lose just as well if you don't execute. It comes down to playing good defense, not turning the ball over, finding the best shot, and putting the ball in the hole. Execution trumps heart and determination. Talent might play a role too, lol.

I want to mentioned all things outside of analytics as opposed to analytics being the one main factor.

 

[anon_0k9zlfz6lz9oy]

That's part of my point. If the game was played on paper, why bother to play? I think KenPom and other analytics formula factor in the name of the team as well even though it doesn't show up with the info given.

I wasn't cherry-picking. Analytics is about the average performance by a team and weighs heavy on previous year perform to help determine a baseline for the current year. The season is long, and there will be ups and downs based on the mindset of "kids." Rutgers lost to Fordham and played several close games with lesser opponents. It wasn't because Rutgers was bad but outworked, and the opponent forced the matchup in their favor. The opponents were able to attack Rutgers' weaknesses then. Rutgers, in return, did the same against several B1G opponents. When I consider these factors such as matchups, Rutgers is in a better position this year than years past. Analytics never consider scouting reports, injuries, preparedness, and coaching decisions. You have to take into account which B1G coach can get his team to outperform expectations, and right now, Coach Pike is a little ahead of several mid-tier B1G coaches in that area.

Exactly. Analytics rely a ton on prior year(s) performance, teams that “get older” often get better. We’re one of them

 

[Scangg]

There is a place for analytics, but it isn't the end all be all. If I was a coach, I would use the analytics and match it up with my eye test and feeling and see where the stats back that up and where they differ. Where they differ see if there is a reason for it that the analytics might not capture or if it is something that I am just missing and make adjustments from there

 

[blockm2]

Exactly. Analytics rely a ton on prior year(s) performance, teams that “get older” often get better. We’re one of them

that's not how "analytics" works to develop preseason predicted ratings. KenPom includes analysis of incoming recruits, returning talent, coaching quality, and previous results to help predict the next season. Returning a bunch of talent definitely improves your preseason prediction the following year. That's why Rutgers finished last season ranked #78 on KenPom and moved up to #63 in the preseason ranks.

 

[anon_0k9zlfz6lz9oy]

that's not how "analytics" works to develop preseason predicted ratings. KenPom includes analysis of incoming recruits, returning talent, coaching quality, and previous results to help predict the next season. Returning a bunch of talent definitely improves your preseason prediction the following year. That's why Rutgers finished last season ranked #78 on KenPom and moved up to #63 in the preseason ranks.

Ken was on a podcast I linked above and he discusses purdue as an example of how last seasons results paired with some returning players goes a long way towards his system liking a team early in the season. He admits that might not be entirely accurate and it still needs to play out.

As an aside, analytically speaking, our team is better without Eugene. About 28% of our possessions end with the ball in Eugene's hands last year. Now that 28% will be spread out between mostly Yeboah and McConnell for the most part who have higher offensive efficiency ratings higher true shooting %'s, get to the line more and then shoot a higher % from the line.

 

[BillyC80]

Pike has said numerous times that this team is better prepared to handle different matchups in the B1G this year. We can play big or small, fast or slow. And as a team we have more capable shooters, especially from deep.

He also says our length is our strength, so this year we will be the ones posing matchup problems for other teams. I like that — we needed to gain the upper hand as a matchup problem for other teams to deal with. For so long it’s been the other way around.

 

[Greene Rice FIG]

Pike has said numerous times that this team is better prepared to handle different matchups in the B1G this year. We can play big or small, fast or slow. And as a team we have more capable shooters, especially from deep.

He also says our length is our strength, so this year we will be the ones posing matchup problems for other teams. I like that — we needed to gain the upper hand as a matchup problem for other teams to deal with. For so long it’s been the other way around.

I get what you are saying AND OOC is not the B1G schedule. With that being said the minutes to Duke and Shaq have to make you a little concerned. Heaven forbid if Myles goes down.

We have the flexibility to match up with teams going small. I couldn't help watching the Michigan game thinking how do we match up with them in a 40 minute game

 

[blockm2]

Ken was on a podcast I linked above and he discusses purdue as an example of how last seasons results paired with some returning players goes a long way towards his system liking a team early in the season. He admits that might not be entirely accurate and it still needs to play out.

Actually KenPom uses several seasons of data, not just the last season.

What's interesting is that Torvik does not use any team level data for preseason predictions, but focuses on individual players data for predictions and still comes up with very similar numbers.

 

[cubuffsdoug]

I get what you are saying AND OOC is not the B1G schedule. With that being said the minutes to Duke and Shaq have to make you a little concerned. Heaven forbid if Myles goes down.

We have the flexibility to match up with teams going small. I couldn't help watching the Michigan game thinking how do we match up with them in a 40 minute game

You make a great point about Michigan. That's why Pike got commitments from stretch type guys where Rutgers can now dictate the match up. You have 6'9 guys big enough to go defense down low on defense but can create match up problems on offense. Rutgers needs more flexible big men to go with the type of guards we now have on the team.

 

[Greene Rice FIG]

You make a great point about Michigan. That's why Pike got commitments from stretch type guys where Rutgers can now dictate the match up. You have 6'9 guys big enough to go defense down low on defense but can create match up problems on offense. Rutgers needs more flexible big men to go with the type of guards we now have on the team.

100%. 6'9''+ bigs that can play inside and out (both O and D) are not luxuries any more.

 

[NewJerseyHawk]

I know several posters here rely on analytics to justify their take on how Rutgers will perform against the rest of the B1G, but did any of them consider other factors like heart or determination? On paper, Nebraska was a clear favorite over the first two opponents, yet they were defeated in both games. I don't think any expert or Nebraska fan considered the other teams were determined to win and were better prepared. The opponents believed in themselves and had something to prove. Also, something else lost in all of this analytics is actual matchups of teams. Most of the data from analytics are overall views on how a team should perform and not individual game matchups. This is where I differ from a lot of posters on here because I believe preparation and matchups have more impact on the outcome of games. For example, according to analytics, Rutgers should have never been able to play Iowa like they did last year. But upon closer inspection, Rutgers had far more advantages over Iowa than what the analytics claimed. RHJ best exemplified Rutgers' strength when he played with his heart and believed his team was better after a painful loss to Iowa earlier in the season. Hell, Bryant did it to Rutgers this year.

I'm old school when it comes to analytics vs. guts, heart, determination. Sports are not played on paper, but in the mind of the athlete, and it reflects on the field, court, or diamond. I don't have to be the biggest guy or the tallest, but I have to want it more and know how to attack my opponents' weaknesses to break their will. This is why I think this team will be special this year. Eugene gave them a "chip on the shoulder" in the way he left, and throw in how the media/fans of the B1G continue to be doubters/haters will only add fuel to the fire.

It's not analytics....it limitations on skillsets.

If a player isn't able to dribble pass shoot and defend, the player is limited to how many ways you can play him.

In football, there's something called a 3 down running back, 3 down linebackers, 3 down defensive lineman and 3 down defensive players.

In football, if you have a running back that can run from I formation, split back set, can catch passes out of the backfield, and is a willing blocker that's able to pick up a blitzing linebacker, you can run more plays and defenses cant gameplan as easily.....Brian Leonard is a 3 down back or prototype...catches, runs, blocks...not as good a strength as a straight line runner as Ray Rice, but a player you can do more things with.

A 3 down linebacker on defense can run, tackle bigger running backs and have the ability to stay with smaller players in pass defense....

A complete defensive lineman isn't just a run stuffer that cannot run or can only plug the holes inside, he can chase a QB against a spread offense.

A complete secondary player on defense can tackle in the run game and cover WRs or TEs on the passing game.

Don't confuse analytics with a basketball player having gaping holes in their overall games, that limits how you can play or impacts how the ball moves (Eugene, Thiam etc)......basketball is a rhythm and coordination sport.....Dribble, shoot, pass, defend, run, and jump. Those are not analytics those are skill sets or limitations.....the more of those items or check marks you have, the better your roster is.

The mid majors can always give a bigger or major program problems because they usually have 5 to 7 players, in sync, and able to have bigs that can shoot from the perimeter.

The bigs we are targeting have that skill set, much more than the burly power players that are from years past.

 

[BillyC80]

I would like to see Shaq get some more minutes (so far he’s played only 12 and 13 minutes in our first two games), including a couple minutes playing with Myles. I’d like to see him be aggressive on the offensive glass and get put-backs or get fouled. He’s a good foul shooter.

I know the problem is 2-fold: one is that we need to continue grooming Myles with as many minutes as he can handle, to get him ready for tougher games to come. And second, with so many capable players, it’s easier and more tempting for Pike to go small. Especially if he wants to run.

That said, I think Shaq can step up and be very productive for us at 15 or 16 minutes per game.

 

[littlenis5]

I think analytics (especially at the beginning of a season) are far less useful in college basketball than in the nba. The NBA teams are playing the same opposition and there isn’t a crazy amount of drop off between teams 1 and 30. NCAA basketball has varying styles, officiating, experience levels and skill sets. The variance between teams leads to very unreliable data in my opinion.

 

[cubuffsdoug]

It's not analytics....it limitations on skillsets.

If a player isn't able to dribble pass shoot and defend, the player is limited to how many ways you can play him.

In football, there's something called a 3 down running back, 3 down linebackers, 3 down defensive lineman and 3 down defensive players.

In football, if you have a running back that can run from I formation, split back set, can catch passes out of the backfield, and is a willing blocker that's able to pick up a blitzing linebacker, you can run more plays and defenses cant gameplan as easily.....Brian Leonard is a 3 down back or prototype...catches, runs, blocks...not as good a strength as a straight line runner as Ray Rice, but a player you can do more things with.

A 3 down linebacker on defense can run, tackle bigger running backs and have the ability to stay with smaller players in pass defense....

A complete defensive lineman isn't just a run stuffer that cannot run or can only plug the holes inside, he can chase a QB against a spread offense.

A complete secondary player on defense can tackle in the run game and cover WRs or TEs on the passing game.

Don't confuse analytics with a basketball having gaping holes in their overall games, that limits how you can play or impacts how the ball moves....basketball is a rhythm and coordination sport.....Dribble, shoot, pass, defend, run, and jump. Those are not analytics those are skill sets or limitations.

Using football is a bad example. Not too long ago players were more complete. Somewhere down the line coaches started to overuse one skillset of a player to the team's advantage. Thus, players became less complete and more specialized. Analytics end up justifying these actions. It's a catch-22. Baseball is the best example of analytics playing too big of a role in the game.

As for basketball or any sport, as you go up levels, a player who is not even close to being a complete player really should be on the team. That reflects on the coach's ability to recruit players to fit his system. Pike is recruiting players who can fit into several categories a.k.a. positionless basketball. I think it becomes harder for analytics to define this style because it doesn't fit neatly into the box.

 

[blockm2]

I think analytics (especially at the beginning of a season) are far less useful in college basketball than in the nba. The NBA teams are playing the same opposition and there isn’t a crazy amount of drop off between teams 1 and 30. NCAA basketball has varying styles, officiating, experience levels and skill sets. The variance between teams leads to very unreliable data in my opinion.

I guess it depends what you are trying to do with "analytics". I don't think anybody seriously thinks they are some iron clad rule going forward, just a pretty good guess. They still line up pretty closely with the Vegas spread even early in the season.

 

[NewJerseyHawk]

Using football is a bad example. Not too long ago players were more complete. Somewhere down the line coaches started to overuse one skillset of a player to the team's advantage. Thus, players became less complete and more specialized. Analytics end up justifying these actions. It's a catch-22. Baseball is the best example of analytics playing too big of a role in the game.

As for basketball or any sport, as you go up levels, a player who is not even close to being a complete player really should be on the team. That reflects on the coach's ability to recruit players to fit his system. Pike is recruiting players who can fit into several categories a.k.a. positionless basketball. I think it becomes harder for analytics to define this style because it doesn't fit neatly into the box.

I don't think coaches are limited to systems anymore. I do think coach Pike struggled with players that just weren't good ball handlers in year 1....we tried dribble handoffs and in some aspects, a system that the Freeman's, Candido Sa, Ibrahima Diallo, Doorson, CJ Gettys and Eugene, really didn't fit....we scrapped that because of turnovers and lack of foot speed, shooting.

Instead, to limit turnovers, the strategy was get a rebound and let Sanders or Nigel Johnson manuever, maybe find Mike Williams and crash the offensive glass and get put backs....doesn't require skill set, but covers Doug's point of heart and determination....it can work to a limited point, as long as the more talented players aren't focused or able to rebound or stop Sanders off the dribble.

You coach to the personnel until you find the pieces that fit.

The missing items in my own checklist are being closed out, 1 by 1.

A) Find a capable big, that can grab a rebound and can bang inside a little bit....BUT has the touch and shooting ability to PULL an opponent's big man, out of the paint and out to the perimeter....Carter and Doucoure have some touch and I think Myles does, but I would not classify them as shooters like 2020 verbal, Dean Reiber is....he has shooting touch

When we played Seton Hall last year, their JUCO big, Romaro Gill, impacted the game by bothering Myles Johnson, Carter and Doorson and forced traveling violations or blocked shots.

If you have a mobile big that can catch and shoot from 15 to 18 feet or even out to the 3 point line, the opposing center, cant park in the lane for rebounds.....if he does, you get an open 3, or the ball moves to another open player.

If you are on defense and you have a slow footed big that cannot get out on shooters at the 3 point line, your going to give up more free looks.

In order to beat the Seton Halls, Michigan States, Illinois and Maryland type programs, you have to have bigs willing to shoot or capable of handling the ball and making basic passes.

B) a big that can be a shot blocker, eraser of mistakes, run and jump and dunk.

Cliff Omoyuri checks every box on defense that you want from a big....he runs like a deer, catches lobs and dunks, is highly competitive, can close out on shooters at the 3 point line and has elite wingspan.

He has very limited offense right now, but impacts a game defensively at a high level. There are more skinny, fast, quick bigs that are more valuable than the burly ones but they fits roles and matchups.

 

[RU848789]

That's part of my point. If the game was played on paper, why bother to play? I think KenPom and other analytics formula factor in the name of the team as well even though it doesn't show up with the info given.

I wasn't cherry-picking. Analytics is about the average performance by a team and weighs heavy on previous year perform to help determine a baseline for the current year. The season is long, and there will be ups and downs based on the mindset of "kids." Rutgers lost to Fordham and played several close games with lesser opponents. It wasn't because Rutgers was bad but outworked, and the opponent forced the matchup in their favor. The opponents were able to attack Rutgers' weaknesses then. Rutgers, in return, did the same against several B1G opponents. When I consider these factors such as matchups, Rutgers is in a better position this year than years past. Analytics never consider scouting reports, injuries, preparedness, and coaching decisions. You have to take into account which B1G coach can get his team to outperform expectations, and right now, Coach Pike is a little ahead of several mid-tier B1G coaches in that area.

I'm a big believer in analytics used properly. Analytics in the beginning of a season, largely based on the previous season's performance, are almost irrelevant, given the turnover in team personnel for every team. The analytics become more and more relevant as the season wears on, as the "data" being fed into the analyses is much more relevant current season/team data.

Anyone quoting Kempom or other sources right now, has zero understanding of data inputs, analyses and projections. Trust me, I know more about those things than most, as the math/stats principles are the same whether one is evaluating likelihood of process failure/performance, based on historical data/trends, for example (which I've done for a living), or analyzing the likelihood of basketball wins based on historical player/team data.

 

[Greene Rice FIG]

Actually KenPom uses several seasons of data, not just the last season.

What's interesting is that Torvik does not use any team level data for preseason predictions, but focuses on individual players data for predictions and still comes up with very similar numbers.

Are you certain of that?

I know they make prediction of player performance, but does it actually use that as a basis of the predictions. I thought everything was based off of expected adjusted offensive and defensive efficiency.

Where I think he overrates us is the expectation that our defense improves

 

[blockm2]

Are you certain of that?

I know they make prediction of player performance, but does it actually use that as a basis of the predictions. I thought everything was based off of expected adjusted offensive and defensive efficiency.

Where I think he overrates us is the expectation that our defense improves

Torvik is very open about the process that goes into his stuff and tweets about it all the time. KenPom is very "secret sauce" and occasionally writes and article spilling out some of the details but that is about it.

As for defense, I think the belief is that players tend to improve defense over their career. More returning players means they should be better than they were last year.

One thing people may not realize is that both KenPom and Torvik are continually making little tweaks to the preseason predictions by comparing each previous seasons predictions to the final rankings and seeing where the biggest errors were. They will never be a perfect prediction of the future because such a thing does not exist, but as it stands they are still pretty darn good and certainly as good as anything else like the AP or coaches poll.

 

[Skull83]

Nebraska was a clear favorite but had an entirely new roster so the spreads were kind of a guess with them.

Also re: Iowa, KenPom had RU about 33% to win at home and 15% on the road. To win as big as they did was probably like 3%, but a) that doesn't account for Iowa not having their head coach, and b) just about every team has a game like that. You can't cherry pick.

Of course analytics can't tell us what's going to happen in a given game. It can just probabilisticly predict a range of outcomes.

I just didn't want the use of the word "probabilisticly" to pass by without some sort of mention :)

 

[rubaseball78]

I know several posters here rely on analytics to justify their take on how Rutgers will perform against the rest of the B1G, but did any of them consider other factors like heart or determination? On paper, Nebraska was a clear favorite over the first two opponents, yet they were defeated in both games. I don't think any expert or Nebraska fan considered the other teams were determined to win and were better prepared. The opponents believed in themselves and had something to prove. Also, something else lost in all of this analytics is actual matchups of teams. Most of the data from analytics are overall views on how a team should perform and not individual game matchups. This is where I differ from a lot of posters on here because I believe preparation and matchups have more impact on the outcome of games. For example, according to analytics, Rutgers should have never been able to play Iowa like they did last year. But upon closer inspection, Rutgers had far more advantages over Iowa than what the analytics claimed. RHJ best exemplified Rutgers' strength when he played with his heart and believed his team was better after a painful loss to Iowa earlier in the season. Hell, Bryant did it to Rutgers this year.

I'm old school when it comes to analytics vs. guts, heart, determination. Sports are not played on paper, but in the mind of the athlete, and it reflects on the field, court, or diamond. I don't have to be the biggest guy or the tallest, but I have to want it more and know how to attack my opponents' weaknesses to break their will. This is why I think this team will be special this year. Eugene gave them a "chip on the shoulder" in the way he left, and throw in how the media/fans of the B1G continue to be doubters/haters will only add fuel to the fire.

I'm in the same camp as you. I do believe analytics can be a tool to give your team an edge. But sometimes game situations, matchups require you to deviate from just going by the numbers. Teams that rely solely on the numbers will not always come out on top.

 

[RUChoppin]

I'm in the same camp as you. I do believe analytics can be a tool to give your team an edge. But sometimes game situations, matchups require you to deviate from just going by the numbers. Teams that rely solely on the numbers will not always come out on top.

Using analytics to give your team an edge is one thing. That can tell you things like not to worry so much about Omoruyi from range, or to push Sanders to his left hand, or to send Johnson to the line instead of giving up a layup, or when to go to a zone based on the lineup on the floor, or whatever. Knowing player tendencies can help you craft a defense or offense. But it still doesn't mean Omoruyi won't go 4/4 from range, or Sanders blows by to his left, or Johnson goes 8/9 from the line - which is why they play the games.

Using analytics as a fan to compare teams is another thing (and is where kenpom and bart come in). And that also has its uses, and prior season performance does play a small role. While teams can and do make huge kenpom swings year to year (80+ rank rise or fall), it's much more likely that they make smaller gains or losses. If a team finished 15th last year, no one is expecting them to be 240th this year... just as no one is expecting a team that was 225th to suddenly jump to 30th. Which is why fans all knew when the schedule was announced that Niagara was going to be an easier game even if we didn't know a single player on their team.

 

[Greene Rice FIG]

[QUOTE="RUChoppin, post: 4150614, member: 2738". Which is why fans all knew when the schedule was announced that BRYANT was going to be an easier game even if we didn't know a single player on their team.[/QUOTE]

 

[RUChoppin]

[QUOTE="RUChoppin, post: 4150614, member: 2738". Which is why fans all knew when the schedule was announced that BRYANT was going to be an easier game even if we didn't know a single player on their team.

But it still was. We had a horrendous shooting night, and were still up double digits most of the game and up 13 with under 8 minutes left. We shot even worse against Bryant than we did against SJU last year, when our doors were blown off.

And as in the first example - just because Omoruyi wasn't a good 3P shooter, doesn't mean he doesn't get hot one night. Just because it's an easier game, doesn't mean it will be an automatic win. That's why they play the games.

 

[rubaseball78]

Using analytics to give your team an edge is one thing. That can tell you things like not to worry so much about Omoruyi from range, or to push Sanders to his left hand, or to send Johnson to the line instead of giving up a layup, or when to go to a zone based on the lineup on the floor, or whatever. Knowing player tendencies can help you craft a defense or offense. But it still doesn't mean Omoruyi won't go 4/4 from range, or Sanders blows by to his left, or Johnson goes 8/9 from the line - which is why they play the games.

Using analytics as a fan to compare teams is another thing (and is where kenpom and bart come in). And that also has its uses, and prior season performance does play a small role. While teams can and do make huge kenpom swings year to year (80+ rank rise or fall), it's much more likely that they make smaller gains or losses. If a team finished 15th last year, no one is expecting them to be 240th this year... just as no one is expecting a team that was 225th to suddenly jump to 30th. Which is why fans all knew when the schedule was announced that Niagara was going to be an easier game even if we didn't know a single player on their team.

That’s my point as a coach. What is happening on the court or field matters. It is more than just crunch the numbers and tell what play to call.

 

[zebnatto]

For me it's not so much Analytics vs Heart and Determination. It’s more like Analytics vs Arrogance, my own. I trust that I know the game and know what I’m seeing. Aka,arrogance.

 

[blockm2]

For me it's not so much Analytics vs Heart and Determination. It’s more like Analytics vs Arrogance, my own. I trust that I know the game and know what I’m seeing. Aka,arrogance.

that is completely true. The sport I played the longest and coached the most is hockey and I have a far harder time understanding the usefulness of advanced stats in hockey compared to baseball or basketball.

 

[Big East Beast]

Chemistry and matchups are more important in basketball than any other sport. college basketball especially has a lot of factors that don’t get accounted for...Girl trouble, school issues, homesickness, etc. how does one quantify ”hustle” or “heart”? To even try is to fail to understand the nature of the game.