The only way to truly refine a process is to be extraordinarily thorough when looking at the results.

That’s been a cornerstone of the Bruski 150 for the better part of a decade and this year I decided to do it really early in the summer.  Part of that was because we’re so damn busy around here and part of it was to leverage some of the internal tools and processes that we’ve refined over the years.

That and I knew this offseason’s research and number crunching was going to be way more labor-intensive and detailed than year’s past — because of all the big-time player movement and tough projections.

All of this, of course, will setup extremely well for us coming off this past ‘public’ season.

Editor’s Note: The Draft Guide is on sale right now and this year we have a few different packages, including the Pro Package which gets you the Draft Guide and also covers you throughout the season with GAMETIME Premium. Our Early B150 release is on September 23 and the B150 gets inserted into the Draft Guide on October 7.  You can preview the Draft Guide right here and you can check out the Guide and other packages right here.

I haven’t and won’t take the time to research how public this past season was, but heading into it my biggest irritation was that there wasn’t enough detail on the board.  Too many easy situations. When all the sharps have all the same action it drives up prices for that set of players, while simultaneously driving down prices on the other guys and depending on how the cookie crumbles, you start to see the Blake Griffins and Marcus Smarts or even a James Harden in 9-cat ADP position totally pay off for the pubs.

That said, ADP never has a chance against us.

And when we’re doing this analysis what we really want to know how we project against the big box sites that have traction with the fantasy public.

We also want to keep tabs on the world of big money competitions and though I was getting knee-capped with fluke injuries throughout the year, we still added yet another medal to the mix, going down to the wire and placing second in the $20,000 FBA.

About half of the guys I play against in these big money leagues continue to use my list against me. Check the testimonial on our Draft Guide sales page for one that has won just south of $100,000 over the last two years in various ringer leagues using our guide.  There’s no honor like being bracketed in a snake draft against top players that know who you’re going to pick.

As for the comparative analysis, as I’ve done for four seasons now, I like to use my old friends at Rotoworld as the measuring stick since they’re the big dogs (woof).

In a truly grueling and somewhat inane exercise, we look at the Bruski 150 to find out how we did both straight up — and then if you read further you’ll see impact analysis that gets really deep in the weeds.

The good news is that we ran the score up to a perfect 4-0 record over the last four years, winning 106-90 in 9-cat at a 54 percent clip (48-40 in the top-100) and an outstanding 105-78 run in 8-cat leagues (53-44 in the top-100) for a 57.4 percent mark.

Anytime one can have that type of an advantage against one of the best in the industry, it’s a great year, and for fantasy players it’s always about backing the right horse when it comes to pairing your own thoughts up with expert analysis.

Trying to take a deeper look (nerd alert), I created a methodology for determining how good or bad a recommendation was.  It has two parts:

1. I use color schemes to measure ranks against each other and in relation to ADP.
2. I assign ranks a grade on an impact scale of 2-5 (1 is understood to be predictions that are incrementally better or irrelevant and those get set aside during impact analysis).

We eventually end up multiplying those together to create a loose system – a starting point if you will – for comparing prediction sets.

RANKING VS. RANKING

The color schemes are:

• Dark Green (massive win, easily had opportunity to draft a player relative to ADP)
• Green (solid win, likely to have had opportunity to draft a player relative to ADP)
• Blue (Just means the prediction was better)
• Yellow (painful loss, prediction put owner in likely position to move the needle backward)
• Red (brutal loss, prediction put owner in likely position to move needle back at significant level)

Not all prediction wins are created equally. Some are dumb luck and have massive impact, which isn’t the sign of a good prediction, and other great predictions have smaller impacts but deserve more credit.  If there was an uncontrollable event not tied to obvious injury risk, such as Gordon Hayward’s broken leg from two seasons ago, then there might not even be an evaluation of predictions. If we’re in the late rounds and somebody is one rank higher, that’s not moving the needle. And so on.

Luckily, we didn’t have anything crazy like the Hayward situation this past year.  Will Barton might have been the closest — setup for huge minutes on a team whose depth wasn’t readily apparent and lost to a fluke hip injury.  You’ll see wins that have been given a reduced impact in a case like that.

Likewise, a player like Caris LeVert, who was on his way to exceeding expectations (and had a higher rating by Rotoworld) — his low end-of-season ranking still actually equates to a win for RW because it was a better prediction.  Since we’re in this rabbit hole you’ll see a different treatment for Kevin Love, who was a known injury risk heading into a high usage season for a team that had no reason to push him at all — in other words a real risk/reward pick — that rank will be looked at with a stricter liability.  Same goes for somebody like Markelle Fultz (ranked 88 in 9-cat by RW), especially when turnovers become part of the equation.

Each rank and evaluation is given the type of scrutiny you’d want to have if you could turn back time and do it all over again.

As we go further down in the draft, when player values start to bunch up, the grading loosens up a tiny bit and color grades won’t reward mild differences.  At the same time a sleeper that can crawl up into early round value will have tremendous impact.

If a player got extremely lucky due to unforeseen injuries ahead of him, we’re not trying to reward or punish predictions as much as we would a prediction that’s based on known variables — one that reflects greater understanding of stat sets, usage rates and the like.

The key to this, for me, is to be brutally harsh with myself and give my competition benefit of the doubt when evaluating these predictions.

Still, there’s a fine line between going over one’s work to make sure we’re constantly improving, and being obsessive over results analysis that’s taking away from my ability to research something else (and this is pretty damned obsessive anyway).

It’s entirely possible I have screwed up on a piece of logic in an example in an attempt to be expedient. I’m pretty sure any shifting results will be within a reasonable margin of error and not take away from the findings.

If you see anything hugely off, just let me know and I’ll make adjustments, but I doubt it’s going to matter.

For what it’s worth, there is a much more detailed results analysis that’ll never see the light of day because it’s too proprietary and way too inane to write about.

Again, looking at it simply, the easiest measure of how the B150 is doing in relation to a great site in Rotoworld, or against ADP, is simply measuring how much the list is winning head-to-head on the predictions themselves.

That evaluation is simply ‘did my pick beat their pick.’

IMPACT ANALYSIS

The impact analysis seeks to determine whether the prediction put the drafter in the position for a gain, avoid a loss and to what degree — and then it aggregates that for the entire prediction set.

As for the impact analysis itself, it is also qualitative but it does trend toward ‘just the facts.’  There, we’re measuring how much distance was there between the predictions and the results.

That scale from 2-5, really is only from 2-4 as a grade of 5 is for Hall of Fame level needle-movers that occur once in a season or maybe even never.

Only one player got a 5 and that was James Harden who nearly lapped the entire field in 8-cat.  Kawhi Leonard got a 5 for being the worst fantasy pick of all time, perhaps, last season.

Last year, picks like Ricky Rubio, Taurean Prince, Dennis Smith Jr., Goran Dragic and Enes Kanter blew holes in rosters, but they were largely not in the disaster range of picks we saw in the previous season.  Victor Oladipo was the worst pick as injuries and a bolstered roster, as well as scattered and erratic play when he was healthy, all conspired to crush owners taking him at the back-end of the first round.  These guys were fours.

There were some second round picks like Donovan Mitchell and Khris Middleton that really dragged ass for most of the year, and in the case of Middleton that one was a real kick in the nads for me because I had him everywhere and he was about as safe as they get. That is, until you find out midseason that Coach Bud had a preseason meeting explaining to him that his role would be cut down, and his steals totals reflected a guy that just wasn’t nearly as engaged on that end.

Depending on the format, something like this would be an impact of two because the spread wasn’t too high but in the early rounds value changes can have a higher weight.

How do outcomes, big and small, either help or hurt a predictor in the ratings? After all, it’s only one prediction out of over 200. For drafters, it’s one of 13-16 picks in standard leagues against 9-11 other owners.

That’s where the impact analysis tries to create a methodology for understanding how impactful the predictions are.

THE MATH

To tie this altogether I created a simple integer system associated with each of the aforementioned colors:

• Dark Green – Massive Win (+6)
• Green – Solid Win (+4)
• Blue – Distinguishable Win (+2)
• Yellow – Painful Loss (-4)
• Red – Brutal Loss (-6)

That, multiplied by the impact rating, is what I’ve found that can mix a results-based review with one that also takes care to measure the realities of the predictions being made.

I can pick a million holes in this system but what it’s essentially saying is a good or bad decision on these impactful players can be worth 2-4 or even 60 times more than (Kawhi two seasons ago) what your run of the mill ‘push’ on a player prediction is.

Most big, impactful predictions in which one site is really high on a guy and the other site is low – and something good or bad happens that is really impactful — the kind that puts all of your readers on one side of the line vs. the other … those are checking in at 10-40 times more impactful than a ‘push.’

The high-end of the impact scale is rare.

Think Pascal Siakam, Nikola Vucevic, Jeremy Lamb, Montrezl Harrell, JaVale McGee, Marcus Smart, De’Aaron Fox, Ricky Rubio, Taurean Prince, John Wall, Victor Oladipo, Danilo Gallinari, Buddy Hield and Jerami Grant rare.  They all got fours.

What this math is doing is saying a prediction with an impact rating of 3-4, when multiplied by the color scale measuring out the likelihood a prediction gained or lost a player, is showing a prediction that is 10-40 times more impactful than being one slot ahead on Al Horford when he finished within a round of where both sites predicted.

That ranking ‘win’ or ‘loss’ isn’t moving the needle too much, but getting Siakam in the later rounds — that bought you well over a half-draft of value.

You’d be doing great if six of your picks each etched away a round’s worth of value in your favor, but you got a guy that did that in one swoop.  And Hoop Ball readers had him all over rosters, whereas fantasy GMs that lean toward RW probably wouldn’t have had him against a Hoob.

So as we do this analysis, I mostly want to understand if the big needle movers were going in my favor.

Because the colors were often influenced by the reality of a prediction situation, there are cases when a color rating has been upgraded or downgraded to better reflect that tension when looking at the totality of an impact rating.

Guys that are hopping or costing 3-4 rounds or more as we get into the middle rounds are your 3s, and players that moved the needle for a few rounds are 2s.

It’s assumed that everybody understands that just because you ranked a guy highly doesn’t mean you’re drafting him way ahead of ADP.

So to put a bow on this, if a dark green prediction was made by one predictor (massive win, easily had opportunity to draft a player relative to ADP) and it had an impact of 4, that score would be:

Absolute Value of Dark Green (+6) * Color Impact (4) = 24

For somebody that made a bad prediction the formula would be the same except it would contain a negative integer for the Color Value and ultimately a negative number for the grade.

We total those numbers up and get a better sense for the weight of the wins and losses.

Again – this is all something that can surely be improved upon, but abstract analysis goes hand in hand with fantasy analysis just as much as the pure numbers do, so I like it.

See if you agree with the color ranks, the impact ratings and even the overall count.

In the end it looks like my predictions carried about 350 more rating points (Total Impact) than Rotoworld’s and we both crushed ADP.  It weighted out as a score of 362-252 in 8-cat and 348-118 in 9-cat. 

***CLICK THE IMAGE TO CHECK IT OUT***

Also, for a link to last year’s B150 you can click here.

PLAYER COMMENTARY

Again … James Harden … wow.  Paul George might have benefited from Russell Westbrook‘s mini-implosion but he was a difference maker. Nikola Vucevic just went bonkers and that helped us out a lot last year. Stephen Curry injuries showed their face again otherwise he was ready to compete for the top slot.  Bradley Beal got a huge gift with the John Wall situation and cashed in. Brook Lopez quietly helped win leagues.  Tobias Harris was a nice example of preseason hype not pushing a guy out of the profit zone.  Myles Turner finally made it work for owners despite a less-than-massive role. Buddy Hield pushed us big time. Pascal Siakam was a classic B150 pick and just narrowly missed the HB6. Danilo Gallinari is in the same spot this season as he was last season and the faders missed a huge, huge gain.  Speaking of the HB6, Jeremy Lamb was almost too easy to pick and he made us look good again.  #Thaditude made Dan Besbris look great.  Donovan Mitchell just couldn’t recover after a very slow start. Montrezl Harrell was another massive win for us. Fading Ben Simmons paid off. ADP won big with JaVale McGee … I didn’t think that was going to be nearly the thing it ended up being.  Blake Griffin had a workmanlike season and I was impressed more by it than any other season he has had because of the staredown against adversity.  Jarrett Allen didn’t kill the hype train but it lost a wheel or two.  The Derrick Favors profit was a nice under the radar win for backers.  The Nuggets had some duds in Jamal Murray, Gary Harris and Will Barton … between injuries and depth that showed up out of nowhere they got crushed.  Mikal Bridges was all over Hoop Ball rosters.  I faded Trae Young and he kicked my ass for it. John Collins‘ ankle injury hurt a lot of sharps and though he came back strong eventually it was a fairly weird season all things considered. Jonathan Isaac never lifted off but even in a totally muted campaign he flirted with his ADP. Luca Doncic was a tale of two formats, paying off for owners in 8-cat and not so much in 9-cat, punting aside.  Hassan Whiteside imploded for owners and especially at the line.  Jaren Jackson’s season sputtered due to injury or tanking … it’s kind of hard to say with certainty.  Nikola Mirotic and Jonas Valanciunas‘ (both HB6ers) seasons were kind of like holding suited AK and losing to a 3-9 offsuit.  You just dust yourself and move on knowing you had precisely the correct read.  Same thing for Kent Bazemore, who was rolling at a top-50 level before injuries and Atlanta tanking took its toll. Taurean Prince got caught up in that same thing in Atlanta and he really hurt owners, so thankfully we didn’t have all the action there.  Kris Dunn (another HB6er) getting injured in the first week derailed his season and you just sort of wink at the injury gods with that. Glad we faded Lonzo Ball. Predictions for Brandon Ingram and Jaylen Brown were dead money.  Ditto Markelle Fultz.

Thanks for reading this far. I can tell you that I haven’t felt this great about a season in a long time, so we should pummel these results.