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2012 NBA Mar |OT| Knicks fans now drinking JD in straight shots

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I love Barks but even he's getting old

Wake me up when he says anything besides "Miami and Chicago gonna meet in the Eastern Conference finals and it will be a drag down series"

Really hoping ECF is not Miami - Chicago just so I can see every single analyst with some egg on their face.
 
Part of Lin's weakness stemmed from a patellar tendon injury sustained the previous season. Still, his squat strength was embarrassingly low for a professional athlete. He could do 110 pounds three times

WHAT THE FUCKING FUCK

Oh god lol
 
Knicks will beat Chicago in 5, when they meet.

AnEZ58ZCEAAfukh.jpg

Yes! Hipster Knicks will take it! I like How Baron is repping cali hard in this pic.
 
In an interview with ESPN the Magazine, Gloria James, LeBron's mom, explains why LeBron went straight for the NBA: "It's not that he wanted to skip college... he was forced to! There was a hole in his vaccination record. That boy just never wanted to take that last shot!"

hairline/10
 
People gonna root for Lebron soon. Y'all can't put him down that much, it brings him sympathy. And te media machine loves rooting for the underdog.
 
Part of Lin's weakness stemmed from a patellar tendon injury sustained the previous season. Still, his squat strength was embarrassingly low for a professional athlete. He could do 110 pounds three times

WHAT THE FUCKING FUCK

damn....I can squat more than double that 15 times....but then again....I got strong legs and I have been doing squats alot
 
I do wonder whether the visual data was entirely necessary to devise the range metric. A simple function based on jumpers seemingly would have spit out almost the same information.

Using 2008-09 for example (% of shots jumpers * eFG% on jumpers * point value of FGA)
Code:
nowitzki,dirk	696.9
bryant,kobe	684.7
williams,mo	640.7
gordon,ben	631.2
terry,jason	601.4
johnson,joe	597.4
lewis,rashard	582.0
allen,ray	582.0
mayo,o.j.	575.6
granger,danny	575.2
is close to the same list and only based on one season.

I'd be interested to see the impact on a teams offense depending on players with high spreads or low spreads on teams with high or low spreads. Or more specifically in lineups with variations.

(I know it's just an introduction. More importantly I wonder what data set it is.)

Sucks that to show the "recent proliferation" they have to cite books from 2004 and 2005. :lol

I liked the conclusion of this paper: http://www.sloansportsconference.co...rt-Ayer-Sloan-Sports-Analytics-Conference.pdf
Obtaining the most talented and productive players on the team is perhaps the most important decision that NBA teams make. Constructing a team that can reach its full potential requires more than just acquiring talented players; these players have to fit well together. This analysis showed that how Big 2s and Big 3s are constructed can significantly affect expected win total, relative to the talent level on the team and the coaching skill.
That's the academia I know and love.

I think the rebounding stuff was already posted but here's the paper, they used camera tracking that follows all ten players and the ball: http://www.sloansportsconference.co...loan-sports-2012-maheswaran-chang_updated.pdf

ESPN summaries: http://espn.go.com/sportsnation/poll/_/id/4691/mit-sloan-conference-paper-previews
Sloan link: http://www.sloansportsconference.com/?page_id=462

One of the talks:
Automating Bill Simmons

With all the discussion about how statistical analysis has disrupted various facets of sports, virtually nothing has been said about sports journalism. That's about to change. Automated Insights, Inc. (via StatSheet.com) has automated the job of writing everything from game recaps and historical retrospectives to trash talk and sports trivia. Last year alone, AI automated over a half million sports stories covering the NFL, MLB, and NCAA Basketball & Football. In this talk, Robbie Allen, CEO and Founder of Automated Insights, will describe how sports journalism is being disrupted by statistical analysis and discuss whether the next Bill Simmons will made of software.
 
This is why you can't take hardcore stat nerds seriously, when they say shit like this:

@DraftExpress: Alagappan's contention Devin Ebanks & Hakim Warrick are as good as LaMarcus Aldridge won't gain him many fans #SSAC
 
I didn't give a fuck.
Tbf, I didn't even know land mines are marked.
are they marked on the map window,
Or can you see them ?

I'd be happy to get a 1 to 1 kill / death ratio in this game.

Lawl they're marked red on the map

also don't play recon if you want to up your K/D ratio. Use the PKP Pecheneg, thing is massively OP especially with the foregrip.
 
This is why you can't take hardcore stat nerds seriously, when they say shit like this:

@DraftExpress: Alagappan's contention Devin Ebanks & Hakim Warrick are as good as LaMarcus Aldridge won't gain him many fans #SSAC
Someone didn't check his sample sizes. (Or look at Ebanks numbers this year before deciding to go all in on the comment.)

Here's his presentation:
From 5 to 13: Redefining the Positions in Basketball

Ever wondered why there are only 5 positions in basketball or how a player’s position is determined? So did we. But now, using Ayasdi’s proprietary data analysis engine and decades of computational topology research at Stanford, we have mathematically categorized players into 13 new positions: the real positions of basketball (which will be unveiled in this presentation). I will describe this revolutionary insight and how it can add tremendous value for coaches, owners, general managers, and the everyday fan. By visualizing the shape of data in terms of performance-based positions, we are able to discover undervalued players, manage in-game decisions, optimize rosters, and draft more intelligently. And this increased granularity in basketball positions is only the beginning. I will also describe how topological data analysis can lead the way to more evolutions in thoughts in basketball and other sports.
 
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