As the Nuggets got off to a rocky start to begin the season, it was hoped that help would be on the way in the form of Wilson Chandler when he returned to play after missing their first six games due to a hamstring injury. And while he has made helpful contributions to an extent, he has clearly fallen short of making the impact many Nuggets fans were hoping for.
For starters, his impact on the court versus off has been essentially neutral. Consider, for example, the discrepancy between Chandler and Mozgov in this regard: (more…)
The Denver Nuggets are now 11 games into the season, and with just five wins under their belt they will be trying to get back up to .500 tomorrow against Dallas.
This is a small sample of games – certainly not significant enough to project what will happen in the latter half of the season, especially considering the fact that the team is adjusting to a new system and missing two of its best players in Danilo Gallinari and JaVale McGee. It is enough, however, to see a picture emerging of how things have gone for Denver so far, and what they might be able to do to improve.
Here we will take a statistical snapshot of where the Nuggets are at now, as well as what they’re doing differently (and – spoiler alert – mostly worse) than last season. This post will focus on Denver’s team offense, so look for more analysis on team defense and individual player performance in future Data Mining installments. (more…)
The analysis of the quality of shots Carmelo Anthony attempts compared to some of the other elite offensive swingmen in the league garnered quite a bit of attention and also quite a bit of feedback from readers.
First of all, I would like to simply clarify what I was attempting to convey. The efficiency with which Carmelo Anthony scores is lower than expected for a player of his skill level to the point people are beginning to question his ability. Based on my observations the gap between Carmelo and other players like LeBron James and Kevin Durant is his propensity to attempt a larger percentage of challenged shots than his fellow star scorers.
I believe I accomplished that through my study, but it was a limited and very basic look at a complex subject. Because of that I wanted to address some of the questions and comments that were posed to me.
We are all well aware of the colloquialism “Beauty is in the eye of the beholder.” Beauty is subjective. We can certainly develop a general consensus of what is beautiful, but we cannot remove the human element of subjectivity. I attended college in Indiana for two years and being from Colorado I was quite unimpressed with the features of the Indiana landscape, there was a friend of mine who was determined to convince me that a flat horizon was prettier than a jagged one. Truthfully, there is beauty in both the mountains as well in the distant horizon. Was one of us right, or more right than the other? That is a question that has no answer.
Some of the world’s great thinkers have tried to determine a scientific or mathematic formula to define physical beauty. Even if one day a formula is developed that can prove who is beautiful and who does not make the cut people will continue to debate the physical qualities of those around us. For every Stanley Hudson, there is a Sir Mix-A-Lot.
When you apply statistics and formulas to something a subjective characteristic, there is always room for dissent. That is the crux of the stats versus scouting discussion. While some believe numbers never lie others will never accept a string of data to contradict what their hearts and eyes tell them, even if it is corrupted by alcohol.
Beauty may be fun to talk about and more fun to ogle, but this is a blog about basketball. Unlike with beauty, statistics and formulas can paint a very comprehensive picture of what a player can or cannot do. The statistics tell us that Carmelo Anthony is not an efficient scorer. While his 28.2 points per game seem to suggest he is an elite scorer, numerous other stats decry that assertion as preposterous. Whether it is his pedestrian 45.8% shooting, his mediocre 54.6% true shooting percentage, or his league average 1.07 points per possession we have ample evidence that Carmelo is inefficient and when we subjectively look at what he does we are misled in thinking he is an immensely talented and versatile scoring machine.
This has troubled me greatly. I believe in the statistics. I know that efficiency is not a subjective matter, but a clear cut numeric certainty. I was one of the first people to decry Melo’s lack of efficiency.
On the other hand, I have seen every professional game Carmelo Anthony has played. The man was put on earth to make buckets. He is big, strong, quick, he can shoot off a jab step, he can shoot off the dribble, he can drive with either hand, even though he rarely finishes with his left, he does not reflect the meager abilities of the volume scorer some are making him out to be. My eyes see all he can do and I cannot believe that Carmelo Anthony is significantly worse offensively than the other more statically efficient superstars in the league.
The one of a kind all-time Nugget great coach Doug Moe’s final season as coach of the Denver Nuggets was 1989-90. Up until then the Nuggets had been one of the best teams in the ABA and then the NBA for 16 seasons. Things took a drastic turn for the worse starting with the 1990-91 season. Paul Westhead brought his comically bad offensive brand of basketball to the Mile High City and the result was far and away the worst season in franchise history. Denver won 20 games and became the laughingstock of the league giving up over 130 points per game. Denver’s defensive efficiency that season was an all-time worst 114.7, tied only by the abysmal 1992-93 Dallas Mavericks and the 2008-09 Sacramento Kings.
Starting with the Paul Westhead experiment the Nuggets fell from one of the better franchises to one of the worst. Over the next 13 seasons Denver won an average of 27.9 games. There was more evidence supporting the existence of the Loch Ness Monster than that the Nuggets had ever been in the playoffs (although to be fair they gave us an exciting playoff run consisting of the well known upset over the Seattle Supersonics in the spring of 1994). The franchise bottomed out during the 1997-98 season as Denver flirted with the worst record in league history pulling out a scant 11 victories.
Somehow over a decade plus of losing the Nuggets were never able to land a true franchise player. The Nuggets topped out as a mediocre team under the duo of Nick Van Exel and Antonio McDyess, who to be fair was an Olympian and memorably scored the game winning basket in the semifinals in Sydney to avoid an upset at the hands of Lithuania back when the USA never lost in international competition, and when Dyess went down with a serious knee injury it made it even easier to tear the team down and start over from scratch.
That reclamation project came to fruition in the summer of 2003. It was then that the Nuggets finally acquired a player talented enough to be considered the cornerstone of a winning franchise. Fourteen seasons after the departure of Doug Moe the Carmelo Anthony arrived in town and there was legitimate hope in Denver for the future of the Denver Nuggets.
I have been an admirer of the increased utilization and availability of advanced statistics in the NBA. I have tried to the best of my ability to incorporate them into my analysis of games in a relevant way. From time to time I have wondered about stats I would love to see such as assists at the rim as opposed to assists on that come from a long jumper or how many calories I burn yelling at the television.
The other night I was watching the barn burner between the Golden State Warriors and the Phoenix Suns and witnessed Anthony Tolliver play nearly every single second of the game, 47:28 to be exact. The thought struck me that 47:28 during that game, where the two teams combined to score 234 points, Tolliver was responsible for covering Amare Stoudemire, carried a significant load of his own team’s offensive burden and was bammed on like few have been bammed on before, was much more grueling than 47:28 of a game between to slower paced teams.
I wondered instead of talking about simply how many minutes a player played in a game, why not look at how many possessions he participated in? Instead of simply tracking who played the most minutes per game or in a season, why not determine who played in the most possessions? I am not sure if anyone has asked this question. As far as I can tell no one has in the manner I am suggesting it.
A simple way to determine possessions could be to divide pace factor by the percentage of minutes played. Using the formula 0.96*(FGA +(0.44*FTA)+TO-ORB) to determine pace for the Suns/Warriors game we arrive with a pace factor of 107.7. Tolliver played 47.42 minutes so he was on the floor for 106.5 possessions.
There are two problems with this idea as I see it. First, certain players or combination of players play at a faster pace than others and simply dividing by minutes is not necessarily an exact determination of possessions every player was on the floor for unless like Mr. Tolliver, they were on the floor for nearly all of them. Secondly, is participating in fewer possessions necessarily more work than having to defend for 16 or 18 seconds every possession even though you may have played in fewer possessions? After all not every player runs the floor during a fast break, but conversely at any one time there are two or three players standing around during a defensive possession and not exerting any energy either. The consensus seems to be that playing at a fast pace is far more strenuous than playing at a slow pace regardless of the quality of defense that is played. The Nuggets have relied on that fact to dominate at home for years.
Some could laugh at my little formula as there are legitimate advanced statisticians who probably already know exactly how many possessions a player participates in during every game. That information is required in order to calculate on and off court stats such as offensive and defensive efficiency ratings by player. As I mentioned above, I suspect no one has really cared much about documenting possessions per game or possessions per season. Let this be the call to look at possessions instead of just minutes and games as a measure of longevity or current service time.
What kind of information could we figure out based on possessions played instead of minutes played? Currently Gerald Wallace leads the league in minutes per game at 41.8 and Monta Ellis is second at 41.4. These two provide a perfect example for how pace can show the difference in the disparity in the possessions these two actually play every game. Using John Hollinger’s team stats the Bobcats are one of the slowest paced teams in the league with a pace factor of 92.9 while Golden State leads the league in pace at 102.7. When we calculate the possessions they would have participated in based on the minutes they played we see that while Wallace was a part of 80.9 possessions, Ellis easily surpasses him at 88.6 possessions per game despite playing four tenths of a minute less.
Apart from trivial things such as who plays more possessions could there be any value to it? Is that kind of information really significant? Bill Simmons talks about the anecdotal evidence that many players lose their legs after playing in 1,000 games. Could that number be lower or higher based on the style of play that player has been a part of during his career? What if a player plays a significant number of seasons on a fast paced team?
We have seen how a fraction of a minute can make a big difference in possessions per game with our example of Monta Ellis and Gerald Wallace. Over the course of nine or ten games Ellis will compile an entire game worth of possessions above Wallace. Over the course of a season Ellis might play in enough additional possessions equal to eight or nine more games than Wallace. Playing for Golden State for seven or eight seasons could possibly take nearly a full season off of a players’ career in additional wear and tear.
That is something a GM might be interested in knowing, for all I know maybe they already do. If not, maybe this can be my little contribution to advanced statistics.