I felt it was reasonable to extrapolate from this for the sake of an anecdotal comment on a discussion post.
It would if the anecdotal comment was at least Bayesian evidence for the claim in question.
But by all means, if you need every statement I made to be qualified with direct evidence, you should seek better sources than my off the cuff remarks. I never claimed otherwise.
I didn’t expect scientific evidence. What I do expect is for valid evidence of some kind, even anecdotal. Your statement was a complete non sequitur. Sorry, if this wasn’t clear.
Agreed, this conclusion is a non sequitur. Regardless of what the average success rate is, some players and teams are more skilled than others and will consistently succeed at a higher rate. People are much better than 50-50 at predicting which team will win a game (and there is a large industry centered in Las Vegas which depends on accurate estimates of these probabilities), which shows that it’s not just random
This is not relevant at all. I am saying that there will be a 0.5 OBP for the game as a whole, not for specific players or teams. The statistics I am talking about having nothing to do with predicting variations in talent within the game.
I actually conjecture that the two of you are committing a non-sequitur by bringing in gambling odds. Do you have statistics about how successful gambling odds have been at predicting the winner or the point spread? Just because there is a business that dreams up the odds does not mean that those odds wind up actually being good predictors of the game. Why do we have million-to-one upsets every season if this is true? Wouldn’t we have to wait for millions of games to have upsets of that magnitude?
The statistical regularities I am talking about are properties of the whole competition itself. And I specifically mentioned that one of my premises is that talent distribution is more balanced in pro sports than in college. How accurate are these odds predictions in pro sports? And how much more accurate are they in college? If college is significantly more accurate than pro, then that certainly is Bayesian evidence for my claim.
The fact that a large gambling industry regularly sets odds for games other than 1:1 is one piece of evidence that the outcomes depend on skill and are not essentially random (analogous to a coin flip). The Vegas favorite does win substantially more than 50% of the time (I could look up a citation if you really doubt that), but there are also plenty of other pieces of evidence besides gambling odds. For instance, teams’ winning percentages have more variability than you’d expect if they followed a binomial distribution with p=.5 (as they would if games were a coin flip). Teams’ winning percentages one season are strongly correlated with their winning percentages the next season. Similar patterns hold true for other statistics that are kept for teams (like points per possession) and for statistics that are kept for individual players. There are also some teams that are consistently successful year after year (like the Yankees, Lakers, and Patriots), and some players whose statistical performance is consistently impressive (like Albert Pujols, LeBron James, and Peyton Manning).
Of course randomness also plays a significant role in the outcomes, but that doesn’t mean that skill is irrelevant. In sports like baseball, basketball, and football, a game consists of many little events with a moderate probability of success where a player’s skill influences their chance of success by a few percentage points. Over the course of a game, these differences accumulate so that the more skilled team wins maybe 70% of their games on average (more when there is a wider disparity in skill, less when the teams are more evenly matched). There are differences between sports based on the nature of the game (in baseball the better team wins more like 60% of the time) and there are differences between levels of competition (as you say, there are wider ranges of ability at the college level and so the better team wins more often). Over the course of a season, the best teams are generally able to emerge and win enough games to make the playoffs (some average but lucky teams make the playoffs as well). Luck plays a large role again in the playoffs, since there are fewer games and teams are often more evenly matched.
The way to go beyond this analysis is to look more closely at the data, as has been done (e.g.) here.
My only point was to speculate that the playing surfaces are engineered to bring about an “optimal” amount of randomness. The link you provided seems to support that the randomness is actually there. Whether it’s perfect 50⁄50 or instead 42⁄58 is just splitting hairs. I still believe that my reasoning was sound. Given that I see random outcomes in sports, the arrangement of the playing surface is a reasonable candidate explanation. Under the premise that athletic talent does not vary widely enough for talent to be the better explanation (which, whether right or wrong, was the premise I had been assuming), it makes organization of the playing surface a more likely candidate. This is why I disagree about it being non sequitur.
I was not claiming that the playing surface dictates every microinteraction within a game and that all of them come out as 50⁄50, just that the game is engineered to be a sellable product, and without randomness it would not be as marketable. I was claiming that talent is not the primary factor, but to be clear, I think that if talent comprises only 58% of the explanation for a team’s record, as the link you provide suggests, that my conclusion is totally appropriate. Talent might be a larger piece of the explanation than chance, but if it is not much much larger than chance, then it’s fair to use natural language to describe talent as not being the primary factor. If you want to split hairs over my linguistic choice to embed thresholds into that statement, that’s fine. But it’s still a reasonable conclusion.
It would if the anecdotal comment was at least Bayesian evidence for the claim in question.
I didn’t expect scientific evidence. What I do expect is for valid evidence of some kind, even anecdotal. Your statement was a complete non sequitur. Sorry, if this wasn’t clear.
This is not relevant at all. I am saying that there will be a 0.5 OBP for the game as a whole, not for specific players or teams. The statistics I am talking about having nothing to do with predicting variations in talent within the game.
I actually conjecture that the two of you are committing a non-sequitur by bringing in gambling odds. Do you have statistics about how successful gambling odds have been at predicting the winner or the point spread? Just because there is a business that dreams up the odds does not mean that those odds wind up actually being good predictors of the game. Why do we have million-to-one upsets every season if this is true? Wouldn’t we have to wait for millions of games to have upsets of that magnitude?
The statistical regularities I am talking about are properties of the whole competition itself. And I specifically mentioned that one of my premises is that talent distribution is more balanced in pro sports than in college. How accurate are these odds predictions in pro sports? And how much more accurate are they in college? If college is significantly more accurate than pro, then that certainly is Bayesian evidence for my claim.
The fact that a large gambling industry regularly sets odds for games other than 1:1 is one piece of evidence that the outcomes depend on skill and are not essentially random (analogous to a coin flip). The Vegas favorite does win substantially more than 50% of the time (I could look up a citation if you really doubt that), but there are also plenty of other pieces of evidence besides gambling odds. For instance, teams’ winning percentages have more variability than you’d expect if they followed a binomial distribution with p=.5 (as they would if games were a coin flip). Teams’ winning percentages one season are strongly correlated with their winning percentages the next season. Similar patterns hold true for other statistics that are kept for teams (like points per possession) and for statistics that are kept for individual players. There are also some teams that are consistently successful year after year (like the Yankees, Lakers, and Patriots), and some players whose statistical performance is consistently impressive (like Albert Pujols, LeBron James, and Peyton Manning).
Of course randomness also plays a significant role in the outcomes, but that doesn’t mean that skill is irrelevant. In sports like baseball, basketball, and football, a game consists of many little events with a moderate probability of success where a player’s skill influences their chance of success by a few percentage points. Over the course of a game, these differences accumulate so that the more skilled team wins maybe 70% of their games on average (more when there is a wider disparity in skill, less when the teams are more evenly matched). There are differences between sports based on the nature of the game (in baseball the better team wins more like 60% of the time) and there are differences between levels of competition (as you say, there are wider ranges of ability at the college level and so the better team wins more often). Over the course of a season, the best teams are generally able to emerge and win enough games to make the playoffs (some average but lucky teams make the playoffs as well). Luck plays a large role again in the playoffs, since there are fewer games and teams are often more evenly matched.
The way to go beyond this analysis is to look more closely at the data, as has been done (e.g.) here.
My only point was to speculate that the playing surfaces are engineered to bring about an “optimal” amount of randomness. The link you provided seems to support that the randomness is actually there. Whether it’s perfect 50⁄50 or instead 42⁄58 is just splitting hairs. I still believe that my reasoning was sound. Given that I see random outcomes in sports, the arrangement of the playing surface is a reasonable candidate explanation. Under the premise that athletic talent does not vary widely enough for talent to be the better explanation (which, whether right or wrong, was the premise I had been assuming), it makes organization of the playing surface a more likely candidate. This is why I disagree about it being non sequitur.
I was not claiming that the playing surface dictates every microinteraction within a game and that all of them come out as 50⁄50, just that the game is engineered to be a sellable product, and without randomness it would not be as marketable. I was claiming that talent is not the primary factor, but to be clear, I think that if talent comprises only 58% of the explanation for a team’s record, as the link you provide suggests, that my conclusion is totally appropriate. Talent might be a larger piece of the explanation than chance, but if it is not much much larger than chance, then it’s fair to use natural language to describe talent as not being the primary factor. If you want to split hairs over my linguistic choice to embed thresholds into that statement, that’s fine. But it’s still a reasonable conclusion.