2023-2024 NBA Fantasy End of Season Rankings

 

2018-2019 NBA Fantasy End of Season Rankings

Waiver Wire: Favor or Sabonis

Derrick Favors or Domantas Sabonis? That’s the question I am asking myself right now after only two games into the 2018-2019 NBA Season. After watching Sabonis hang around on the waiver wire after two good games I can’t help but ask myself is the time to move now and get Sabonis before he makes too much noise and another manager picks him up. 

After 9 years in the league I know what I am going to get out of Favors; good FG%, marginal FT%, no 3PMs, decent rebounds, no assists, and average steals and blocks. There isn’t much excitement in owning him but he is fairly consistent and having a PF that can increase my FG% with very few TO’s is a benefit to any team, especially my guard heavy team. Sabonis, on the other hand, is entering his 3^rd season, and should be past the normal learning curve that most players experience and should start tapping into that potential that OKC originally saw in him. The question is, what does he give me that Favors will not?  

A purely statistical analysis at this point would to be inappropriate as there is such a small sample size from this season, nor do I feel comfortable forecasting Sabonis’ future statistics based on just two seasons of play. Therefore, I will use a qualitative decision making tool, namely one from the portfolio of Multi-Criteria Decision Making (MCDM) techniques, namely the Weighted Sum Model (WSM). All this means is that I will rate a series of criteria and make a qualitative assessment from 1-10. Whomever has the highest score wins and I promise to make my decision based on this (pinky promise). 

So how am I going to rank them? Statistically, Sabonis and Favors are fairly comparable and based on last season, both played most of the season and roughly the same amount of minutes. Based strongly on last year’s stats Favors, as a starter, had an edge on FG%, blocks, and TOs. Sabonis, as a bench player, was a better FT shooter, rebounder, and assist maker. All other stats are tantamount to each other. Therefore I am going to say that both provide a benefit though the net change will be negligible. This means that I am going to use selection criteria that is fairly subjective, if not just for their employment. This criteria will be as such will be based on opportunity meaning that they will have more opportunity to improve from last season. So I will try to qualify the opportunity they have to increase their production How I will measure this is to compare the bigs around them and their respective health, their paint touches, their usage rate, personnel changes around them, minutes they are projected to play. I will rate their situation on a scale of 1-10, with 1 being the lowest and 10 being the highest.

Bigs around them (Weight: .1): 

These are the guys that are eating in to each of the player’s opportunities, or helping them, depending on how you look at it. I look the bigs around them as taking away scoring and rebounding opportunities, so I see having the bigs around them as taking away opportunity. So let’s see who is around them:

Sabonis: Myles Turner is a promising young player who has not quite filled into his potential. Interestingly he averaged less rebounds per game than Sabonis did last year, so it is not as if he is really cutting into Sabonis’ stats. On top of that Sabonis is seen as the off-the-bench C/PF, who plays behind Turner and starts for him in his absence. Not sure to rank this but I can only assume that more minutes would be better for Sabonis, which he is getting so far this season. 

Favors: Rudy Gobert is known not only as a big time shot blocker but he also gobbles up a lot of rebounds around the rim at a rate of 10.7 last year. These are elite numbers and no doubt cut into Favor’s ability to get these boards instead. As a cornerstone of the team Gobert is going nowhere any time soon and with the exception of last year has not been very injury prone. 

Advantage Sabonis. Score 7-5.

Paint Touches (Weight: .15):

This is all about opportunity where they should be getting lots of opportunity to score. Neither player is a 3PT marksman, therefore they should avoid the long “2” and shoot from the paint instead. How do we measure how much they shoot from the paint? Paint touches is a good start and lets you know how often a team is feeding their big man. Last year Steven Adams, of OKC, led the league in Paint Touches with 13.0 per game. Data from https://stats.nba.com/players/paint-touch/   

Sabonis: Last year Sabonis got 6.0 paint touches a game, ranked for 28^th. His teammate, and main paint touch rival Myles Turner, had 3.7 paint touches a game. Sabonis clearly was the preferred player in the paint and this trend continues so far with Sabonis averaging an absurd 13.5 paint touches this year, which will surely go down. Though this is a small sample size (n=2) this is still a step in the right direction but may be an aberration. 

Favors: Last year Rudy Gobert was 3^rd in the league behind Andre Drummond and Steven Adams. Even at 10.8 paint touches a game this still allowed Favors to get 7.7 paint touches a game, or ranked 11^th in the league. Favors is averaging 7.0 paint touches this year with Gobert averaging 11.5, or approximately the same from last year. 

Advantage Sabonis. Though the small sample size it appears that the Pacers are going to Sabonis in the paint. Based on potential alone Sabonis gets the nod. Favors is not too far behind as the 7 paint touches is still nice. Score 8-6.

Usage Rate (Weight .25)/Personnel Around them (.2): 

What is usage rate? According to BasketballReference.com it is the estimate of the percentage of team plays that a player uses while he is on the court, with the higher the percentage the better. This is a good metric to use as it is not only a measure of efficiency but what a player is contributing while on the court. This is also a function of, and correlated to, the personnel around the player (aka who else is using plays). Data was collected from https://stats.nba.com/players/usage/

Sabonis: Indiana’s superstar is Victor Oladipo, who averaged a 29.4% usage rate last year and is currently maintaining a 31.1% rate. Last year Sabonis ranked second with a usage rate of 21.2%, ahead of Turner who had a 19.7%. Sabonis is clearly a part of the rotation and is currently rocking a usage rate of 18.9%. Sabonis’ main competition, on the team, is and remains Oladipo and Turner. 

Favors: The Jazz has one primary superstar in Donovan Mitchell but hsave also invested in their other cornerstone, Rudy Gobert. In a crowded rotation last year, Favors had the 7^th highest usage rate at 17.9%. The Jazz maintained the same core group of players from last year, minus Rodney Hood, and though Favors usage rate is high after two games at 22.0%, there is no reason to believe that he can or will maintain this. The Jazz have too many mouths to feed. 

Advantage Sabonis. Usage Rate matters. Score 7-5. Also, being 2/3 in the pecking order is better than being 5-7. Score 8-3.

Projected minutes played (Weight .3):

Similar to the other metrics projected minutes played measures opportunity. You need to be on the court to collect those fantasy statistics.

Sabonis: Domantas is not a starter, though he is first off the bench. He averaged 24.5 minutes per game last year and is currently averaging 25.5 minutes a game. I usually want me starters to average 30 minutes a game so he is currently sitting well below that threshold. Much like the “Free Boban” movement, Sabonis is making use of his time on the court but there is no telling how much he could improve with 30 minutes a game. 

Favors: He is a starter who averaged 28 minutes a game last year and is currently average 22.5 minutes. Again small sample size but I think he will average at least 27 minutes a game as Utah’s front court is fairly thin. 

Advantage Favors. Starters get more stats, at least most of the time. Score 7-4. 

Ok, let’s do some quick math and figure out whom I am going to keep or pick-up. First let’s create our weighted matrix using the metrics we calculated above. 

Player/Metric	Bigs	Paint Touch	Usage Rate	Teammates	Minutes
	.1	.15	.25	.2	.3
Sabonis	7	8	7	8	4
Favors	5	6	5	3	7

Once we have the matrix above the math on this is easy as all we do is multiply each of the criterion by the weight and sum up the total for each player (sumproduct in Excel).  Any other complicated math? Nope, that’s it, so who wins? At a score of 6.45 to 5.35 Sabonis takes it. 

Ok, so what am I going to do now? Well I told you that I would follow the results so I am picking up  Sabonis for Favors. Ultimately I am betting on the upside because right now they are basically the same player. I am bucking the axiom of a “bird in hand” but this is why we play the game, juggling risk vs. reward.

20180926, Basketball Stats ... by on Scribd

2018-2019 NBA Fantasy Rankings

Greetings from the back of my spreadsheets. I am providing to you, my fellow fantasy basketball enthusiast a glimpse into what I do for a hobby: play fantasy basketball. I am 18 year veteran of fantasy basketball and the winner, and loser, of several leagues. Whether playing in casual or competitive leagues if there is one thing that I love to do is to watch my players succeed in the virtual arena. Similar to the stock market, I would hit refresh on my eight opened ESPN/Yahoo sports tabs following my players every play-by-play hoping that they would rebound, score, assist, or steal their way to a great game. Sometimes players had good games, sometimes they had bad games but what I soon learned is that the players all followed a similar trend that with a little bit of investigation I could figure out. I call this investigation data analysis, or stat crunching.

​As a result of trying to gain a competitive edge I started to use expert’s picks and usually had mixed results. I started noticing that sometimes the experts would pick a player because they would grace ESPN’s TOP 10 highlights or would be a popular or well-known picks. It seemed to me that this was a major criteria for their selections. Therefore, in the pursuit of better data driven decision I decided to formulate my own rankings, based on the vast knowledge that my Stats and Calculus professors required me to learn. 

​I started off by using a technique that a majority of the experts use now to rank players, the “Z-score”. The z-score, is a way to standardizing a NBA basketball player’s statistics by fitting all the players’ statistics against a normal distribution, or bell shaped curve. Analysts will take all player’s statistics and scale the data down so that the average player will have a score of close to 0. In this manner they take all of their sortable statistics and standardize them so that they can compare them together and against each other. Just think about how you would compare a player’s points scored per game (PPG) compared against their Free Throw Shooting Percentage (FT %). With that in mind, these scores are then figured out for each category and then added together, with whomever had the highest score would be ranked higher. Overall it is an intuitive way of ranking that majority of basketball stats junkies understand. Unfortunately it leaves a lot to be desired as it does not consider some of the fundamental lessons that we have learned throughout the years of playing. Ultimately some stats are scarcer than others. How do we account for this amongst other things?

​What I started to use three years ago is a method of ranking that is part of the platform of Multi Criteria Decision Making (MCDM) methods named the Technique for Order Preferenceby Similarity to an Ideal Solution, or TOPSIS. TOPSIS is a mathematical way of doing literally its name describes, rank by a predetermined preference. The best choice, or ultimately the best ranking, should have the shortest distance to the most positive ideal solution and be the furthest away from the least ideal, or negative, solution. This model allows for a user to select a weighing criteria, determined by scarcity of statistics, and find the solutions that are closest to the ideal solution, or how the ideal player should perform. Think Andre Drummond’s rebounding, mixed with Draymond’s stealing, a little bit of Rudy Gobert’s blocking, and James Harden’s overall production without the turnovers. That is what TOPSIS aims to lead you to. 

​The strategy of using TOPSIS is to rank players based on these “super-player” attributes and select them in order to find the players that have the statistics that will most help and least hurt your team. Some of the picks are flashy. Some of them you won’t agree with. But they do provide insight into building the foundation of your team (principally picks #1-#10) before you have to worry about specialists. So without any more fanfare, though I plan to describe this process more in detail in the notesof my blog, I present my rankings. If you are uneasy just believing me I have included their z-scores as well and have taken the liberty of averaging them against these rankings to provide a “consensus” ranking similar to what FantasyPros does. Enjoy.