Amazing, yet totally believable headline from earlier this season: “Pelicans’ Anthony Davis drops rare 5×5 statistical game…”
Jaw-dropping headline from last week that I’m still not over: “Nurkic’s NBA-best 5×5…..” <RECORD SCRATCH>
That’s TWO five-by-fives already this season! A 20/20 5×5 from Jusuf Nurkic!?! The NBA is definitely “Where Amazing Happens”.
Quick nostalgia video for my fellow StarCraft and/or Aliens fans regarding the quote I hear in my head each time a 5×5 is mentioned:
I’ll try to keep it relatively short this week, as the concepts won’t be new. I thought it was high time we look at statistical scarcity again, now that we have a significant sample of this season’s stats. We’ve talked about how statistical performances compare across categories based on player rater values and just how good stats have to be to offset those that negatively affect us among other similar ideas. But this time, I’m back to comparing the scarcity of our counting stats, this time through the lens of the 5×5.
As a grizzled fantasy veteran like many of you, I have most players’ typical stat sets from the last few years stuck in my head. It takes a long time for me to change my opinion, regardless of how hot or cold someone gets for a few weeks or so. I generally go with logic and think “small sample size — regression is coming”. But I wasn’t always so boring. It wasn’t always this way…
Flashback to the year 2000…
(Okay, sorry. I’ll try to stay focused. Stupid gifs of everything in history at the click of a button.)
…You’d find me in my dorm room, navigating my way through my first fantasy basketball season, loving our super-fast ethernet connection (no more AOL dial-up like at home!). Putting off homework by manually adding up my team’s stats on Yahoo each night in the pre-StatTracker days. It was an 8-cat Roto league (still my game of choice), so I didn’t need to get too crafty with weekly games played, matching up against specific teams, or checking NBA opponents. I’d been a big NBA fan, but I’d lost touch somewhat since my Bulls had disbanded in 1998. So, my main strategy was simple: Look at stats for the last month, and pick up whoever the best available guys were (I remember a guy I’d never heard of, Bo Outlaw, providing some sneaky stats for weeks and months at a time). Give them a couple of games to see if they’d keep it up, and if not, swap ’em for the next hot thing. Could it be so simple? Well, I ended up winning that league that season. And most seasons after that. You may not have found my friends in my league adding up their teams’ stats at 4 a.m., I guess.
Thought I’d have a little throwback fun this week. Who’s tired of the Jordan vs LeBron debates? Yep. Me too. Jordan never lost a finals! LeBron hasn’t lost a conference finals in 8 years! Look at his teammates! Look at HIS teammates!… blah, blah, blah. Here’s the real question we care about in our world: Who was the better fantasy player? Now, it’s not quite the same argument as greatest of all time, because there are at least a handful of other players that have been more valuable fantasy-wise than one or both of these guys, but lets see if we can make some sense out of their fantasy careers. Thanks once again to Basketball Monster for having historical player raters.
I used to play a lot of fighting video games. Street Fighter 2, Mortal Kombat, and the like. I think I aged out around the time they were up to Ultimate Mortal Kombat 3 and Super Street Fighter 2 Championship of Everything Ever Edition Turbo. But I know they kept going, adding more characters, merging games. And that’s sort of what I wanted to do this week. I’ve written a decent amount about stuff like category specialists, how scarce the stats in each category are, how volume skews percentage stats, and just how detrimental the percentage and turnover performances of your players can be. Well, today, we’re going to witness a 2018 battle royale of sorts. Using Basketball Monster, I took the standard deviation value of each individual’s statistical performance in each category, and ranked them. Other sites have slightly different values due mostly to alternate assumptions and weights. There are some writers out there who have explained fantasy basketball standard deviation values in depth and in ways that are much more exciting than those in my old college statistics books. So, if you’re really curious, you can find out more with a little searching. I’ll just say that, in general, a standard deviation score of 2 means that the performance is roughly better than almost 98% of the rest of the league. A score of 3 is about where you’d expect the best performance in the league to be, as it’s usually around the 99.9th percentile. Same thing for negative values, just reversed. So, if you see a value exceeding 3, and I’ve shared some of those insane standard deviation scores from the last few decades in previous posts, it’s super-valuable (turbo edition 64?). Some categories don’t have anyone reaching 2 or -2, meaning the numbers are more bunched up together. But some have some extreme outliers. That’s what we’re looking for today.
Boban’s gotta be in there somewhere, right?
I present to you the most and least valuable individual category performances of the year (per-game through 12/4, with some small sample players removed).
This season of 50-point games and JaVale McGee relevance is already about 25% complete. How are your teams looking? We should have a pretty good picture of what we can expect from our lineups and most players, so where can we go from here? Let’s get creative. I’ve been talking about how the practice of ignoring categories that aren’t affecting us can give us an advantage (even if we weren’t trying to punt categories), as it presents a market tilted in our favor. Shaking up the values of players and customizing them to our teams is a great way to make some effective trades. Trades that are more likely to get accepted, because they can more easily be win/win deals. Today, I’m going to give a variety of lists of multi-category “punts” to help identify targets that often go undervalued, in addition to those that complement punting teams best.
I’ve gone on and on about how most categories get overlooked. That’s something that can give savvy managers an advantage. The masses, if they aren’t looking closely at player raters and rankings, may essentially be “punting” the ignored categories like steals, for example. As I often mention, I truly think most fantasy managers subconsciously weigh points, rebounds, and assists more heavily than the other stats. It’s understandable, as that’s how most media outlets report stats, but it’s ridiculous to do so in fantasy, as all categories are created equal.
So, first up, here’s a list of some startable players with the biggest jumps in 9-cat per-game value (per Basketball Monster through 11/25) when we ignore Points, Rebounds, and Assists. These 6-category rankings should give us the players that are most undervalued, especially by casual fantasy players. Think of them as the thinking-man’s fantasy all-stars, fittingly led by it’s perpetual mascot.
I recently met a conspiracy theorist. He seemed so proud and satisfied that he had the inside scoop on so many topics (“You know what’s going on in Cuba, don’t you?”), while the rest of us only know what the government wants us to know. Well, I went down a rabbit hole to which he directed me just for kicks. Wow, there are a lot of crazies out there trying to obtain knowledge that no one else has, regardless of how insane it is. Shout out to Kyrie.
I realized, though, that I can relate. At least when it comes to fantasy basketball. There’s certainly a draw to uncovering a conspiracy and being part of only a small group of people that feels wiser than everyone else. Or, more relatably, being the only person to know a secret. This is how I felt the first time I manipulated a fantasy bball player rater. I was finally confident enough in my Excel skills to subtract categorical columns for punt rankings. I had decided to go all-in on a punt free throw percentage 8-category Roto dynasty team. Removing the FT% category dramatically changes the value of many players. I realized that I could trade players for much more than they were worth to me while acquiring players for much less than they were worth to me. Obviously, the downside was taking last place in a category. But since I was near the bottom in FT% anyway, I only lost maybe 2 points there while gaining something like 7 or 8 total points combined in other categories. The problem in a league like that is that I would’ve needed to get first in nearly every other category to win it all. I peaked at second place.
Yeah, yeah, you’re aware of the simplest of punt strategies. I know. But, aside from overrating rookies in dynasty drafts, this is really what I’m most passionate about: the concept of ignoring categories that aren’t going to help or hurt you.
I’m going to keep it pretty simple this week. I’d like to check in on category leaders to help figure out who the best specialists might be this season. There’s a lot of value sitting out in the free agent pool just waiting for you to stream it. Adding and rotating through these category specialists applies in roto leagues when you notice individual categories in which you stand to gain a few points. But, this information will probably help the most in head-to-head leagues where you should be swapping out at least a couple players each week (assuming you can) to customize and maximize your stats in a way that nets you the most category wins against your opponent.
“So… you’re just pasting an NBA stat leaders’ page?” Nope. I’m only going to feature players rostered in less than 50% of Yahoo leagues. Italicized players are owned in less than 25%. For shooting percentages, I’m using Basketball Monster’s values that are weighted for volume. Next week, I’ll do sorta the opposite and list the punt specialists (value rankings with each individual category removed), as well as the rankings according to some other helpful stat combinations. I’ll leave out the flukey or injured players to save you some time here, as well.
So, we’re three weeks into another joyous fantasy basketball season. The hot waiver pick-ups are gone or have fizzled out. Hope you got the ones with lasting value. Pretty soon, the sample sizes will be large enough to know that what we’re seeing is more or less legit. For now, there’s still a lot of regressing to the mean yet to come. Hot and slow starts will mostly fade away, and the players will be themselves over the long haul. Not everyone, as plenty of players take significant leaps or stumbles for the entire season, whether it has to do with a change of scenery, personnel, and/or usage. It can be tough to figure out whose rebounds and steals changes, for example, will stick. However, we can trust with a good amount of confidence that most players shooting percentages will end up relatively close to their previous numbers. And, this early in the season, when, say, Serge Ibaka goes 15-for-17 and then 8-for-8, percentages can be way out of line and skew value if you’re looking at rankings in a trade scenario.
It’s the summer of 2000. Who wouldn’t want to draft Shaquille O’Neal, fresh off a MVP season, in the 2nd round of a fantasy draft? Sure, his free throw percentage was terrible, but you could make up for that with a couple FT% specialists, right? Plus, Shaq still finished as the 15th most valuable player for that MVP season despite the horrendous 52.4% from the line (9-category per-game rankings according to Basketball Monster). He’d go on to, more or less, repeat his 29/13 with 3 blocks and the most dominant field goal percentage in the league (more than twice as valuable in that category as anyone else). The FT% took a slight dip to 51.3%, but this was the height of “Hack-a-Shaq”, and his free throw attempts increased from about 10 to around 13 per game. He fell all the way to the 39th ranked player. And what’s worse, his FT% negated nearly all of his positive contributions.
Last week, I discussed some of the unheralded stats: Threes, Steals, and Blocks. At this point, many experienced fantasy b-ballers know to pay a good amount of attention to those, though. Today, I’ve got three more categories to ponder that may get ignored just as much. However, these three can also hurt your team as opposed to, at worst, adding zero stats in a category (yes, a zero in a category can be a negative to your team, but I’m talking stats that can get far more negative than the best players’ positive value in the category). Today’s categories are Field Goal Percentage, Free Throw Percentage, and Turnovers. The reason I bring these up is to get you focused on these stats as much as you are on the popular ones like points, rebounds, and assists. They count for just as much, and since your competition likely doesn’t value them as much, you can get an advantage in your league.
We’ll get back to Shaquille and his efficiency categories, with his best-in-the-league FG% and worst-in-the-league FT% in a moment. But, let’s start with:
Check out this excerpt from a 2002 ESPN The Magazine article detailing an interview that provided the substance for what I think was an And1 ad campaign featuring Kevin Garnett:
Q: Are you overpaid? KG: Hell no. If anything I’m underpaid, with everything I do. That’s a ridiculous question. I have to do everything for this team. Q: Are you tough enough to play in the Western Conference? Maybe Minnesota should move to the East. KG: Man, I’ve been in the Western Conference for seven years. Holdin’ it down. Nobody there scares me. Look at my numbers. You know my rap sheet. Q: What are your numbers? KG:Twenty, ten and five. Twenty, ten and five. Three years in a row. And I’m rounding down. Who else has done that? Q: What does that get you? KG: It gets you what it gets you.
“20, 10, and 5”. I remembered hearing that line repeatedly around the time I started playing fantasy basketball, and it always stuck with me as the gold standard baseline for greatness (big men dominated the top of the fantasy landscape) and a main reason KG was a fantasy first rounder for years. Points, rebounds, assists. That’s all anybody every really seemed to talk about. And, to this day, those are the numbers to which we all pay the most attention, whether or not we know better. Triple-doubles, double-doubles. “How did LeBron do tonight?” – “Oh, great! 27, 11, and 7!” KG would impressively go on to hit at least 20/10/5 for three more seasons, but that leaves out half his great numbers! Garnett had up to 1.7 steals per game and up to 2.2 blocks per game during his career, and that really sent him to the top of the fantasy rankings. Top 5 in 8-category and 9-category per-game rankings for at least those six seasons. If he’d started playing a decade or so later, I’m sure he’d have been hitting a three or two each game, as well.
Today, I’m going to extoll the virtues of three stats that often get overlooked. Threes. Steals. Blocks.