Book Review- The Second Machine Age

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson and Andrew McAfee, 2014

second machine age

The abilities of machine learning/artificial intelligence have improved greatly lately. You can expect the abilities of computers to continue to grow exponentially. This book calls the economic upheaval caused by computers “The Second Machine Age”, with the first machine age being the physical abilities of machines in industry and on assembly lines. Just as the first machine age displaced the jobs of many manual workers, you can expect the second machine age to displace the jobs of many intellectual workers. Are the computers/robots coming for your job? Maybe not immediately, but probably eventually.

I’m fascinated by what is going to happen when a significant portion of the population (the proportion that does not play well with technology) is totally unemployable. Their skills, if any, are unneeded because computers/robots have automated away their job. Is this portion already at 5%? What if it hits 40% due to automation of more jobs? A huge amount of people are employed as drivers of some sort. Self-driving cars are probably 5-20 years away. Who’s going to pay for a driver when self-driving cars/trucks/taxis are cheaper and safer? And that’s just one technology. What if 90% of people are eventually not employed and all money goes to 10%? Vast inequality should be planned for. Will we institute a minimum income for everyone? Will we vastly expand social programs? What happens?

I listened to this book on CD. I thought it was pretty interesting, though it does start slow. The links below are related and supplementary.

Relevant other books/links:
The Great Stagnation by Tyler Cowen
Average is Over by Tyler Cowen
Video: Humans Need Not Apply by C.P.G. Grey

Offense/Defense Strength for NCAA Basketball Teams

I built a model akin to my NFL betting model that gives each NCAA basketball team an offense coefficient and defense coefficient. The offense coefficient represents how many points above average the team is expected to score. The defense coefficient represents the number of points above average the team gives up on defense. Thus, the prediction for the Kentucky Wildcats in their first game against Hampton on Thursday would be

Wildcats prediction = mean score + Kentucky offense coefficient + Hampton defense coefficient = 66.84 + 11.26 + 5.20 = 83.3

In non-neutral site games, there is a 1.5 point addition to the home team and 1.5 subtraction from the away team. I just consider all NCAA games neutral site games, despite one fanbase normally being closer.

Some interesting notes about the offense, defense, and overall (offense-defense) coefficients for each team:
1. 5 tourney teams have a negative overall coefficient: N Dakota St (-1.2), Lafayette (-1.5), Robert Morris (-2.0), TX Southern (-3.9), and Hampton (-6.9).
2. The best non-tournament team is the Florida Gators at +12.3 overall (1.0 above average on offense and allow 11.3 points less than average on defense). Other good non-tournament teams were Illinois (+12.1 overall), Miami (+11.3), Minnesota (+11.2), Stanford (+11.0), Syracuse (+10.8), and Vanderbilt (+10.6).
3. The worst team in the 351-team NCAA this year was Grambling, with a -28.3 overall coefficient (slightly more terrible than Kentucky (+27.8) is awesome).
4. The 3rd best teams got a #2 seed: Arizona (+23.5)
5. Ohio State is the 11th best team in the nation by this metric, with a +19.0 overall. They are a #10 seed.
6. Indiana is 37th best in the tournament (+11.9), making their #10 seed appropriate.

Here are the offense, defense, and overall (offense-defense) coefficients for all NCAA teams. In Tournament = 1 for tournament teams and 0 for non-invitees. Thus, the Kentucky Wildcats are the strongest team in the field (obviously) and the Hampton Pirates are the weakest.

Team,In Tournament,Offense,Defense,Overall
Kentucky,1,11.25960306,-16.57318803,27.83279109
Wisconsin,1,9.115671943,-14.68432901,23.80000096
Arizona,1,12.40809289,-11.11432937,23.52242226
Duke,1,17.39059681,-5.474975439,22.86557225
Villanova,1,12.56022043,-9.750654145,22.31087458
Virginia,1,1.805020066,-19.6773667,21.48238677
Gonzaga,1,13.04747379,-8.207122475,21.25459626
Utah,1,6.235327975,-13.92119987,20.15652785
North Carolina,1,16.66244203,-3.31654816,19.97899019
Ohio St,1,11.70042955,-7.2802724,18.98070195
Oklahoma,1,9.516156382,-9.102721052,18.61887743
Kansas,1,10.847503,-7.259423534,18.10692654
Notre Dame,1,14.45949873,-3.48248196,17.94198069
Iowa St,1,16.29635719,-1.636044635,17.93240182
Baylor,1,6.647621065,-11.11977291,17.76739397
Louisville,1,5.711179709,-11.3695919,17.08077161
Michigan St,1,9.189558128,-7.119456585,16.30901471
Texas,1,5.376367565,-10.36320387,15.73957144
Butler,1,4.93350344,-10.61264068,15.54614412
West Virginia,1,11.20009757,-3.89932305,15.09942062
Georgetown,1,7.42827321,-7.230947384,14.65922059
Wichita St,1,4.651308633,-9.995758816,14.64706745
Iowa,1,6.336631418,-8.207893165,14.54452458
Xavier,1,10.87039446,-3.376334191,14.24672865
BYU,1,17.86536531,4.230098624,13.63526668
Arkansas,1,14.01347164,0.536361194,13.47711045
Providence,1,7.403010189,-5.813107989,13.21611818
Davidson,1,13.88838228,1.01852462,12.86985766
Oklahoma St,1,5.063517753,-7.624884597,12.68840235
Maryland,1,5.260574173,-7.371466112,12.63204029
NC State,1,7.616708866,-4.959370502,12.57607937
SMU,1,4.67010389,-7.631662887,12.30176678
VA Commonwealth,1,8.8726408,-3.412780509,12.28542131
Purdue,1,5.715193397,-6.303977647,12.01917104
Northern Iowa,1,0.495397908,-11.47349107,11.96888898
Indiana,1,13.71244271,1.849928621,11.86251409
Georgia,1,5.246141864,-6.450354598,11.69649646
San Diego St,1,-2.880506251,-14.16467921,11.28417296
St John’s,1,6.507087478,-4.69088311,11.19797059
Mississippi,1,9.030576775,-1.882864659,10.91344143
UCLA,1,8.400883474,-2.449929352,10.85081283
LSU,1,10.02410226,-0.453060944,10.47716321
Boise St,1,5.079430746,-5.175186054,10.2546168
Dayton,1,2.775584578,-7.259294845,10.03487942
Oregon,1,9.985403864,-0.010738454,9.996142318
SF Austin,1,8.348589146,-1.539344608,9.887933754
Cincinnati,1,-1.807592319,-11.44615051,9.638558194
Buffalo,1,9.329858916,0.376408928,8.953449987
Valparaiso,1,0.15520871,-6.691053229,6.846261939
Georgia St,1,2.769249095,-3.563044014,6.332293109
Harvard,1,-2.762281708,-7.711752657,4.949470948
New Mexico St,1,1.066980071,-3.361550501,4.428530572
UC Irvine,1,0.376535358,-3.892116978,4.268652337
Wyoming,1,-4.218604803,-8.141358983,3.922754179
Wofford,1,-4.276188352,-7.693278281,3.417089929
Northeastern,1,1.325841741,-1.486661314,2.812503056
E Washington,1,9.798037894,7.785407386,2.012630508
UAB,1,2.006062461,0.426427327,1.579635134
North Florida,1,4.791860857,3.619065323,1.172795534
Belmont,1,5.770113057,4.616958017,1.15315504
Albany NY,1,-2.375194726,-3.38255515,1.007360424
Manhattan,1,2.220402645,2.050065064,0.170337581
Coastal Car,1,-0.728206243,-0.892579425,0.164373182
N Dakota St,1,-5.175674886,-3.955373727,-1.22030116
Lafayette,1,5.527468982,7.001728579,-1.474259597
Robert Morris,1,-0.696576107,1.333643168,-2.030219274
TX Southern,1,-1.080044415,2.798343678,-3.878388093
Hampton,1,-1.718260145,5.197706658,-6.915966804
Florida,0,0.996067845,-11.33750928,12.33357713
Illinois,0,5.882193229,-6.2444598,12.12665303
Miami FL,0,4.779889622,-6.567139055,11.34702868
Minnesota,0,10.00642965,-1.237734366,11.24416402
Stanford,0,7.736198076,-3.249591011,10.98578909
Syracuse,0,4.023281648,-6.733093345,10.75637499
Vanderbilt,0,6.16894547,-4.425327329,10.5942728
TCU,0,3.516369996,-6.973175241,10.48954524
Texas A&M,0,2.998572582,-7.322350985,10.32092357
South Carolina,0,1.336055443,-8.373694142,9.709749586
Rhode Island,0,1.005201751,-8.011815191,9.017016942
Alabama,0,3.066385132,-5.901676955,8.968062087
St Mary’s CA,0,3.917216482,-4.898745141,8.815961623
Colorado St,0,7.02833787,-1.554791865,8.583129736
Arizona St,0,6.02690996,-2.476773765,8.503683726
Michigan,0,0.877866017,-7.593902313,8.471768329
Richmond,0,0.108423704,-8.135753766,8.24417747
G Washington,0,0.738652361,-7.485228151,8.223880511
Kansas St,0,0.56311114,-7.291471364,7.854582504
Connecticut,0,0.546308868,-7.256523952,7.80283282
Old Dominion,0,-2.583918381,-10.22972448,7.645806098
Pittsburgh,0,2.774298368,-4.782916275,7.557214643
Seton Hall,0,5.075389094,-2.188857722,7.264246816
Illinois St,0,3.310061022,-3.868796641,7.178857663
Temple,0,0.483946384,-6.619582979,7.103529363
Penn St,0,3.408222207,-3.578297067,6.986519273
Colorado,0,3.742999136,-3.106254664,6.8492538
Tulsa,0,-1.01719754,-7.812501635,6.795304095
Murray St,0,9.662263907,2.877460936,6.784802971
Creighton,0,3.596779064,-3.175563273,6.772342337
WI Green Bay,0,0.795453247,-5.864845897,6.660299145
C Michigan,0,6.725853259,0.069096683,6.656756576
Clemson,0,-2.285782543,-8.833162927,6.547380384
Memphis,0,3.122080408,-3.129715098,6.251795506
Tennessee,0,-0.267994897,-6.457120999,6.189126102
Georgia Tech,0,-0.030724561,-6.151813181,6.12108862
Louisiana Tech,0,5.253106516,-0.799176422,6.052282938
Marquette,0,1.165456059,-4.86156217,6.027018229
Toledo,0,8.578017189,2.728087637,5.849929552
San Diego,0,-1.025327829,-6.594883322,5.569555493
Florida St,0,3.282123663,-2.213762341,5.495886005
UTEP,0,1.581857002,-3.904976051,5.486833053
Northwestern,0,-0.737608463,-6.01573569,5.278127227
Hofstra,0,9.833004258,4.62114795,5.211856309
Nebraska,0,-2.696072651,-7.847774707,5.151702056
Boston College,0,2.500114823,-2.638529732,5.138644555
UNLV,0,3.293830258,-1.828045426,5.121875684
Yale,0,0.185367176,-4.894610087,5.079977263
Oregon St,0,-6.489120942,-11.54293161,5.05381067
Iona,0,12.28698365,7.270781316,5.016202334
Pepperdine,0,-2.178180403,-7.175283574,4.997103171
La Salle,0,-1.03653008,-5.857597335,4.821067255
Washington,0,4.156108142,-0.618865535,4.774973677
Bowling Green,0,-0.424303031,-5.08513009,4.660827059
Santa Barbara,0,1.896456566,-2.589519779,4.485976346
California,0,1.534051995,-2.890370245,4.424422241
Sam Houston St,0,-0.926371758,-5.228182006,4.301810248
S Dakota St,0,3.428221093,-0.687036962,4.115258055
St Bonaventure,0,1.373054482,-2.697319726,4.070374209
Akron,0,-0.17813198,-4.189479929,4.01134795
William & Mary,0,5.483831383,1.511800151,3.972031232
Portland,0,4.28489534,0.496046721,3.788848619
Wake Forest,0,5.154787094,1.470510028,3.684277066
Cleveland St,0,-1.730484523,-5.315557291,3.585072767
Massachusetts,0,4.503996491,1.110706026,3.393290465
San Francisco,0,2.97935543,-0.405108429,3.384463859
UC Davis,0,3.458902259,0.189379994,3.269522265
Texas Tech,0,-3.005620131,-6.050017736,3.044397606
Utah St,0,1.266698553,-1.750632529,3.017331082
E Michigan,0,-0.069977381,-2.923037331,2.85305995
Kent,0,-1.372694097,-4.184148526,2.811454429
New Mexico,0,-3.3279385,-6.026888695,2.698950195
Hawaii,0,4.965419489,2.276244932,2.689174557
Vermont,0,-2.038140582,-4.636356501,2.598215919
Long Beach St,0,0.957426082,-1.609106209,2.566532291
NC Central,0,-5.512535911,-8.067988604,2.555452693
DePaul,0,6.187197872,3.670162662,2.51703521
Evansville,0,1.896656286,-0.569755704,2.466411989
Auburn,0,5.267195181,2.807346188,2.459848993
Charlotte,0,8.209179424,5.972521795,2.236657629
W Michigan,0,4.242939745,2.229910356,2.013029389
Loyola-Chicago,0,-3.446573915,-5.40821774,1.961643825
ULL,0,6.463796634,4.664775705,1.799020929
Stony Brook,0,-1.742689358,-3.529018073,1.786328715
Mississippi St,0,-3.041778822,-4.760184818,1.718405996
W Kentucky,0,2.773718189,1.296815487,1.476902702
E Kentucky,0,-0.454632855,-1.901837933,1.447205078
USC,0,2.54844928,1.285713324,1.262735956
MTSU,0,-3.692019264,-4.619562797,0.927543533
Ga Southern,0,-2.976804328,-3.727874955,0.751070627
Cal Poly SLO,0,-6.592110567,-7.306101365,0.713990798
Morehead St,0,0.622256647,-0.053323399,0.675580046
Virginia Tech,0,0.763899728,0.306864648,0.45703508
Columbia,0,-2.86763066,-3.275757059,0.408126399
Missouri,0,-2.917653075,-3.063573326,0.145920252
Canisius,0,-3.63213316,-3.619946054,-0.012187106
Oakland,0,7.252157994,7.334114287,-0.081956293
St Joseph’s PA,0,-3.225039641,-3.087439478,-0.137600163
Santa Clara,0,-2.412328389,-2.227071553,-0.185256836
Princeton,0,1.086117859,1.33258349,-0.246465631
SC Upstate,0,-1.599832112,-1.245130116,-0.354701996
Rutgers,0,-3.394469345,-2.992765939,-0.401703406
FL Gulf Coast,0,-2.281612458,-1.87648001,-0.405132448
Washington St,0,6.566108571,6.978309486,-0.412200914
Rider,0,-2.316368694,-1.741170355,-0.575198339
Montana,0,0.144235667,0.803924985,-0.659689318
N Illinois,0,-2.806057038,-2.034933594,-0.771123445
Chattanooga,0,1.059492109,1.852849239,-0.79335713
UNC Wilmington,0,1.268403577,2.064525859,-0.796122282
St Francis NY,0,-1.463772125,-0.636722353,-0.827049773
Indiana St,0,1.66175481,2.578660309,-0.916905498
Quinnipiac,0,2.200067917,3.158129085,-0.958061168
High Point,0,1.900669964,2.868520511,-0.967850547
Mercer,0,-3.614651612,-2.623296311,-0.991355302
Dartmouth,0,-3.522651292,-2.423691485,-1.098959806
Monmouth NJ,0,-3.118569654,-1.831730572,-1.286839082
Colgate,0,-1.683976191,-0.322847015,-1.361129177
ULM,0,-7.644379082,-6.252107969,-1.392271113
American Univ,0,-9.122727235,-7.610655179,-1.512072056
TN Martin,0,-0.499581707,1.013601276,-1.513182984
George Mason,0,-0.885944355,0.703640161,-1.589584516
Lehigh,0,-0.491650582,1.123443391,-1.615093973
St Peter’s,0,-7.07505769,-5.396715626,-1.678342064
Air Force,0,-0.941462427,0.737962363,-1.67942479
Detroit,0,2.115567729,3.810647191,-1.695079461
New Hampshire,0,-2.884150021,-1.157638038,-1.726511983
Fresno St,0,-0.231732419,1.626203669,-1.857936088
NJIT,0,0.373113264,2.343502036,-1.970388772
Tulane,0,-2.868324193,-0.779169392,-2.089154801
IPFW,0,0.35985052,2.565490089,-2.205639568
Rice,0,-2.580430452,-0.370472159,-2.209958294
Fordham,0,-0.967340218,1.256931199,-2.224271417
Winthrop,0,1.399083918,3.624457093,-2.225373174
James Madison,0,-0.613762944,1.653648672,-2.267411616
Cornell,0,-4.846105379,-2.431923334,-2.414182045
UT Arlington,0,4.028142635,6.448471202,-2.420328567
Houston,0,-1.729708745,0.76160125,-2.491309995
Northern Arizona,0,-2.072605406,0.508621675,-2.581227082
Bucknell,0,0.57640413,3.166643318,-2.590239188
Norfolk St,0,0.495207954,3.112632027,-2.617424073
UT San Antonio,0,2.613381863,5.245869209,-2.632487346
Pacific,0,-2.297038228,0.420016776,-2.717055004
Duquesne,0,5.711658999,8.595133114,-2.883474115
Radford,0,0.375015585,3.287930187,-2.912914601
Northwestern LA,0,14.22450637,17.23378187,-3.009275506
East Carolina,0,-3.712050931,-0.611834791,-3.10021614
Oral Roberts,0,0.419988084,3.630057691,-3.210069607
Charleston So,0,1.943414414,5.205023434,-3.26160902
Miami OH,0,-0.415209768,2.866242056,-3.281451824
ETSU,0,2.471736539,5.912212678,-3.440476139
S Illinois,0,-6.157573226,-2.700533146,-3.45704008
Denver,0,-6.565471623,-3.102845593,-3.46262603
Ohio,0,0.936426727,4.488960018,-3.552533292
CS Sacramento,0,-1.288859955,2.273181756,-3.562041712
Incarnate Word,0,7.402196168,10.98088307,-3.578686899
UC Riverside,0,-2.784617596,0.884981819,-3.669599415
St Francis PA,0,-5.233595214,-1.397568201,-3.836027013
Gardner Webb,0,5.001842641,8.857140967,-3.855298326
South Dakota,0,1.06893918,4.949976457,-3.881037277
Loy Marymount,0,-2.883562536,1.051555203,-3.935117739
Ball St,0,-1.503277507,2.495559789,-3.998837296
WI Milwaukee,0,-2.349322524,1.678582951,-4.027905474
Texas St,0,-9.269064091,-5.215836621,-4.05322747
Boston Univ,0,0.982871516,5.107878359,-4.125006843
TAM C. Christi,0,-5.456215952,-1.260433079,-4.195782874
MD E Shore,0,-0.998300235,3.252011703,-4.250311938
SE Missouri St,0,-0.3483239,3.951216473,-4.299540373
W Carolina,0,0.922386169,5.346115246,-4.423729077
Idaho,0,6.066138593,10.51756029,-4.451421693
North Texas,0,-3.647670704,0.839912215,-4.487582919
E Illinois,0,-5.989082465,-1.445421848,-4.543660616
Mt St Mary’s,0,-5.56359457,-0.934424092,-4.629170478
Bryant,0,-2.195357379,2.518425931,-4.71378331
Ark Little Rock,0,0.547467282,5.298663714,-4.751196432
Elon,0,-0.240354127,4.511183657,-4.751537785
Bradley,0,-8.37664549,-3.592307402,-4.784338088
Holy Cross,0,-2.928888871,1.896180072,-4.825068943
Army,0,2.772384133,7.849327861,-5.076943729
Tennessee Tech,0,0.826315101,5.905836797,-5.079521696
Missouri St,0,-5.616194331,-0.512754846,-5.103439485
Drexel,0,-7.625302857,-2.477440262,-5.147862595
Wright St,0,-4.752418536,0.401458146,-5.153876683
South Florida,0,-3.593079227,1.715871166,-5.308950393
N Kentucky,0,-2.04398871,3.289056831,-5.333045541
CS Bakersfield,0,-6.588338225,-1.203099074,-5.385239151
UNC Asheville,0,1.03391908,6.583641461,-5.549722381
N Colorado,0,3.018838259,8.580062997,-5.561224738
Towson,0,-5.610522012,0.093104863,-5.703626875
Sacred Heart,0,3.919829138,9.693034193,-5.773205055
CS Northridge,0,-1.852929275,3.98571557,-5.838644845
Nevada,0,-5.594594316,0.298487562,-5.893081878
St Louis,0,-6.386710515,-0.470035566,-5.916674949
Siena,0,2.208504419,8.241529361,-6.033024942
Brown,0,-0.470367695,5.57497357,-6.045341265
NE Omaha,0,8.090192449,14.18870399,-6.098511539
Marshall,0,-0.746519536,5.420578306,-6.167097842
Edwardsville,0,-3.205022245,3.049733763,-6.254756008
Drake,0,-5.775067885,0.484753049,-6.259820935
Weber St,0,-5.211653126,1.068924264,-6.28057739
Grand Canyon,0,-0.32446925,6.027983046,-6.352452296
Portland St,0,-0.402864831,5.95596998,-6.358834811
UCF,0,0.335665962,6.712550523,-6.376884562
Col Charleston,0,-9.322829761,-2.943662251,-6.37916751
Florida Intl,0,-5.612210274,0.827017816,-6.43922809
Youngstown St,0,3.484870625,9.980632972,-6.495762348
Delaware,0,-1.259239891,5.399725796,-6.658965687
Navy,0,-7.905711572,-1.190380118,-6.715331454
Seattle,0,-7.203145318,-0.448100722,-6.755044596
Missouri KC,0,-4.373760955,2.382282137,-6.756043093
Alabama St,0,-1.178961346,5.660603645,-6.839564991
FL Atlantic,0,-4.85179156,2.065263763,-6.917055323
Hartford,0,-5.869510096,1.061818559,-6.931328656
UNC Greensboro,0,-2.101102747,4.909633938,-7.010736685
Penn,0,-5.478463923,1.677572732,-7.156036654
IUPUI,0,-6.724834299,0.512185498,-7.237019797
Howard,0,-9.390006486,-2.074338898,-7.315667587
Long Island,0,-1.419296926,5.933529667,-7.352826593
IL Chicago,0,-3.1003701,4.327083122,-7.427453222
Troy,0,-3.157157684,4.44839154,-7.605549224
Lamar,0,-2.009235767,5.676802956,-7.686038722
Southern Univ,0,-7.471546862,0.311625385,-7.783172247
CS Fullerton,0,-2.307880755,5.514573128,-7.822453883
Fairfield,0,-8.120293998,-0.199553647,-7.920740351
Delaware St,0,0.148669139,8.129307178,-7.980638039
South Alabama,0,1.781854161,10.03723734,-8.255383176
Samford,0,-1.200207241,7.085620712,-8.285827953
Prairie View,0,-1.715864997,6.70249149,-8.418356487
McNeese St,0,-4.587011656,3.838570856,-8.425582513
Niagara,0,-3.196683094,5.232712619,-8.429395713
Appalachian St,0,-4.901144942,3.578007172,-8.479152114
VMI,0,8.928041167,17.64065666,-8.712615495
Lipscomb,0,1.33506164,10.07697614,-8.741914502
SE Louisiana,0,-3.374385406,5.487942074,-8.862327481
Arkansas St,0,-5.398963875,3.581289279,-8.980253154
MA Lowell,0,-7.216633641,1.879708372,-9.096342013
Loyola MD,0,-8.271863321,1.040031911,-9.311895231
Furman,0,-6.2476101,3.1446892,-9.392299299
Campbell,0,-8.76002721,0.634768747,-9.394795957
Marist,0,-7.520361471,1.918982391,-9.439343862
Southern Miss,0,-7.505483926,1.964986683,-9.470470609
Wagner,0,-0.593898955,9.089303794,-9.68320275
New Orleans,0,-1.686582927,8.081602857,-9.768185784
Idaho St,0,-6.497892904,3.63759944,-10.13549234
Jackson St,0,-10.01001642,0.226799093,-10.23681551
Southern Utah,0,-0.966744398,9.541382221,-10.50812662
F Dickinson,0,-2.323451205,8.312831631,-10.63628284
North Dakota,0,-2.267420963,8.398692971,-10.66611393
Houston Bap,0,-1.160294673,9.803285498,-10.96358017
Jacksonville St,0,-7.752549583,3.291861773,-11.04441136
Austin Peay,0,-4.948736469,6.20067653,-11.149413
Longwood,0,-3.22795396,8.086301508,-11.31425547
Utah Valley,0,-11.17313608,0.232802957,-11.40593904
Montana St,0,-6.179269638,5.520533516,-11.69980315
Binghamton,0,-9.675342685,2.065992679,-11.74133536
Citadel,0,-7.348762049,4.484239091,-11.83300114
W Illinois,0,-7.913298207,4.962000519,-12.87529873
TX Pan American,0,-8.234011898,4.795520933,-13.02953283
Chicago St,0,-12.15960884,1.24969145,-13.40930029
Ark Pine Bluff,0,-10.38078872,3.080324954,-13.46111367
Tennessee St,0,-11.1653286,2.302534256,-13.46786286
Bethune-Cookman,0,-13.98129854,-0.381930917,-13.59936762
Nicholls St,0,-8.15134421,5.745910117,-13.89725433
Morgan St,0,-7.660614348,6.452347539,-14.11296189
Stetson,0,-4.971428348,9.326606928,-14.29803528
UMBC,0,-10.81376601,3.534758145,-14.34852416
Liberty,0,-8.985824119,5.573471015,-14.55929513
NC A&T,0,-10.51972732,4.041732807,-14.56146013
Alabama A&M,0,-8.929831708,5.785563128,-14.71539484
Presbyterian,0,-9.771610519,5.032594445,-14.80420496
Jacksonville,0,-6.317416348,8.558836805,-14.87625315
S Carolina St,0,-10.34049138,4.655714868,-14.99620625
Central Conn,0,-9.767524448,5.419515775,-15.18704022
Abilene Chr,0,-9.442635939,5.760057141,-15.20269308
Kennesaw,0,-6.998986438,8.588047505,-15.58703394
Coppin St,0,3.650463135,19.36687571,-15.71641258
Maine,0,-5.85945427,9.951243892,-15.81069816
Savannah St,0,-12.70797586,3.265338239,-15.9733141
San Jose St,0,-12.75446765,3.340359086,-16.09482673
Alcorn St,0,-6.881942805,11.57196986,-18.45391266
Cent Arkansas,0,-5.478359998,15.61111881,-21.08947881
MS Valley St,0,-4.42321955,17.19380121,-21.61702076
Florida A&M,0,-13.3187347,10.49868716,-23.81742186
Grambling,0,-18.45707411,9.855333563,-28.31240767

Book Review- Event History Modeling: A Guide for Social Scientists

Event History Modeling: A Guide for Social Scientists
by Janet M. Box-Steffensmeier and Bradford S. Jones

event history modeling

This book covers event history modeling in depth. It was suggested by George Ball during his job talk at IU. I needed the insights provided by this book for better understanding of my call center project. In that project, we are looking at the determinants of how long a customer will stay on hold once they hear an announcement about the expected wait. We model the patience of the customer as certain covariates vary. This book encompasses the field of survival analysis, which is named for the medical literature that studies how certain covariates affect survival rates.

The book is about 200 pages, and I highly recommend it for someone that is new to survival analysis or event history modeling. It covers the Cox Proportional Hazards model in depth, along with various parametric models. It covers issues of model selection, time-varying covariates, mis-specified models that go against assumptions, heterogeneity, and multiple events. Of most use to me was Chapter 8, which discusses diagnostic methods for determining if your model is performing properly. I will apply these methods to my project to ensure it is working as expected (and to appease referees).

Papers Read Jan-Feb 2015

I’ve started to keep the first page of each academic paper that I read in a binder, marked with my notes about the paper’s content and usefulness. I started doing this in June 2014. Between June and December, I read 89 academic papers. In January and February 2015, I read 69 more.

By Decade:
1980’s: 2
1990’s: 2
2000’s: 18
2010’s: 45

Journals (with more than 1 paper read):
Management Science: 18
MIT Sloan Sports Analytics Conference: 6
Manufacturing & Service Operations Management: 4
IEEE Transactions on Knowledge and Data Engineering: 3
Information Systems Research: 3
Operations Research: 2
Production and Operations Management: 2
Queueing Systems: 2

Total citations among 69 papers: 18,108
Max citations: 5,532
Mean citations: 266
Median citations: 31.5
Papers with less than 5 citations: 20, though some are very new

If interested, the full Excel list is here.

Sloan Sports Analytics Attendees from Professional Teams

In addition to my SSAC 2015 wrap-up, I thought it might be interesting to show which U.S. professional sports teams were represented at SSAC 2015. I received an attendee list at sign-in, and it lists the organization for the vast majority of attendees who wanted to share such information. For example, I am listed as attending from Indiana University, along with 3 other people I don’t know (reach out if you read this!). I will list how many attendees there were from each of the teams in the Big 4 sports leagues in the U.S. below. If an individual wants to remain anonymous by not listing an organization, they are not included below.

NFL:
Arizona Cardinals: 0
Atlanta Falcons: 5
Baltimore Ravens: 3
Buffalo Bills: 3
Carolina Panthers: 2
Chicago Bears: 2
Cincinnati Bengals: 0
Cleveland Browns: 7
Dallas Cowboys: 16
Denver Broncos: 1
Detroit Lions: 0
Green Bay Packers: 0
Houston Texans: 0
Indianapolis Colts: 1
Jacksonville Jaguars: 0
Kansas City Chiefs: 3
Miami Dolphins: 7
Minnesota Vikings: 0
New England Patriots: 3 (+4 under Kraft Sports Group)
New Orleans Saints: 2
New York Giants: 4
New York Jets: 0
Oakland Raiders: 2
Philadelphia Eagles: 7
Pittsburgh Steelers: 2
San Diego Chargers: 0
San Francisco 49ers: 2
Seattle Seahawks: 1
St. Louis Rams: 3
Tampa Bay Buccaneers: 0
Tennessee Titans: 0
Washington Redskins: 0

MLB:
Arizona Diamondbacks: 1
Atlanta Braves: 1
Baltimore Orioles: 0
Boston Red Sox: 12
Chicago Cubs: 0
Chicago White Sox: 0
Cincinnati Reds: 0
Cleveland Indians: 3
Colorado Rockies: 1
Detroit Tigers: 0
Houston Astros: 3
Kansas City Royals: 0
Los Angeles Angels: 2
Los Angeles Dodgers: 2
Miami Marlins: 1
Milwaukee Brewers: 3
Minnesota Twins: 0
New York Mets: 3
New York Yankees: 3
Oakland Athletics: 0 (no Billy Beane?!?)
Philadelphia Phillies: 0
Pittsburgh Pirates: 2
San Diego Padres: 1
San Francisco Giants: 2
Seattle Mariners: 1
St. Louis Cardinals: 0
Tampa Bay Rays: 2
Texas Rangers: 6
Toronto Blue Jays: 0
Washington Nationals: 3

NBA:
Atlanta Hawks: 3
Boston Celtics: 12
Brooklyn Nets: 4
Charlotte Hornets: 5
Chicago Bulls: 3
Cleveland Cavaliers: 6
Dallas Mavericks: 4
Denver Nuggets: 2
Detroit Pistons: 4
Golden State Warriors: 9
Houston Rockets: 9
Indiana Pacers: 6
LA Clippers: 4
Los Angeles Lakers: 1
Memphis Grizzlies: 4
Miami Heat: 11
Milwaukee Bucks: 5
Minnesota Timberwolves: 2
New Orleans Pelicans: 2
New York Knicks: 2
Oklahoma City Thunder: 6
Orlando Magic: 7
Philadelphia 76ers: 9
Phoenix Suns: 5
Portland Trail Blazers: 1
Sacramento Kings: 12
San Antonio Spurs: 1
Toronto Raptors: 4
Utah Jazz: 4
Washington Wizards: 4

NHL:
Anaheim Ducks: 0
Atlanta Thrashers: 0
Boston Bruins: 0
Buffalo Sabres: 0
Calgary Flames: 1
Carolina Hurricanes: 2
Chicago Blackhawks: 0
Colorado Avalanche: 0
Columbus Blue Jackets: 2
Dallas Stars: 0
Detroit Red Wings: 0
Edmonton Oilers: 1
Florida Panthers: 1
Los Angeles Kings: 1
Minnesota Wild: 1
Montreal Canadiens: 0
Nashville Predators: 2
New Jersey Devils: 0
New York Islanders: 0
New York Rangers: 0
Ottawa Senators: 0
Philadelphia Flyers: 2
Phoenix Coyotes: 0
Pittsburgh Penguins: 1
San Jose Sharks: 0
St. Louis Blues: 0
Tampa Bay Lightning: 1
Toronto Maple Leafs: 5
Vancouver Canucks: 0
Washington Capitals : 1

All NBA teams are represented. 20/32 NFL teams. 19/30 MLB teams. 13/30 NHL teams.

Now, I don’t think # of attendees is a great indicator of analytics prowess (16 attendees, Dallas Cowboys, really?), but shouldn’t you at least send ONE attendee? I’m disappointed, but not surprised to see that my Cincinnati teams failed to do so.

Sloan Sports Analytics Conference 2015 Recap

SSACLogoWebsite-2015

Last week I attended my first MIT Sloan Sports Analytics Conference as a student attendee. Sloan is a lot different than most conferences that would be relevant to someone in Operations Management. For one, it only accepts 8 research papers for presentation. For two, most of its presentation slots are panels where the participants come from industry. Specifically, sports teams or companies.

Overall, I had a good time. Most of the research paper talks I attended were good, and maybe half of the panels I attended said something interesting or funny. It’s a common refrain from attendees that panelists are trying to be intentionally vague or misleading so that they don’t give away any competitive advantage. While I understand this, it makes for some boring panels.

I got to hang out with Charles Glover, a former co-worker at Booz Allen that was at the conference showing off Booz’s data science capabilities. Booz Allen co-sponsored the conference enough to have a permanent “data visualization” zone in one of the conference rooms. Apparently Booz Allen is helping run Major League Baseball’s replay headquarters since last year’s introduction of coaching challenges. Here is an overview of how Booz Allen is using data science in sports.

I queried Matthew Berry, resident fantasy expert, for tips for Maria’s new class. During the next January term at DePauw, she will be teaching “How to Use Data Science to Win at Fantasy Football”. That should be fun. Berry had suggestions for looking at draft value by position and number of draftees still in lineups in Week N.

I wrote a few notes from some of the interesting panels and presentations, which I’ll share here:

Michael Lewis (author of Moneyball and The Blind Side) was on a panel with Daryl Morey (Rockets GM), Jeff Van Gundy, and Shane Battier. Lewis had a few good lines, including “You can’t be too stupid to play baseball”. He said that he wanted to interview Battier for this article because Battier was a lab rat who could understand the experiments his coaches and GM were putting him through. Battier, for his part, had a good line about Heat coach (misspelled) Erik Spoelstra: “Spoelstra told me, ‘Don’t dribble, don’t post-up, don’t offensive rebound. Just catch and shoot or catch and pass'”. Battier’s teammates LeBron, Wade, and Bosh got a little more freedom, I imagine.

Mark McClusky, from WIRED magazine, shared some interesting thoughts on the evolution of performance in sports. “The only competitive advantage is to learn faster.” Once you implement a strategy or an insight, others will copy it, so you need to keep learning to stay good. He also shared some research on sleep and suggested that everyone needs to sleep more to be at peak performance. Some suggested books for reading include Better, Faster, Stronger by McClusky, The Sports Gene by Epstein, and Better by Gawande.

There was a new fantasy platform called Reality Sports Online that will begin a big advertising campaign to get players this year. It is a more complicated/intense version of fantasy involving mullti-year contracts, negotiations, and rookie drafts. It’s meant to mimic the general manager experience more than current fantasy leagues.

Dan Rosenheck, writer at the Economist sports blog, talked about how Spring Training statistics had some predictive value, contrary to popular belief. A brief writeup of his presentation is here.

Brian Burke kept his cool in a silly football analytics panel, which was impressive. He said that an NFL game boils down to 11 minutes of gameplay, which means a defensive coordinator could watch all of his squads plays and grade his players 5 or 6 times in half an hour. As such, it could be years before player tracking data beats insights from tape watching.

Boston was cold and snowy. I don’t suggest visiting in February for tourism.

Other Recaps of SSAC 2015:
What it’s like to be a woman at SSAC
Soccer Analytics Panel at 2015 SSAC: Not a waste of time
2015 Sloan Recap: Where are the Analytics?
SSAC 2015 Takeaway – Accepting Yes For An Answer
The Value of a Good Analytics Program by Brian Burke

Book Review- Einstein’s Dreams

Einstein’s Dreams
by Alan Lightman, 1992

einstein's dreams

Lightman’s novel is short, but beautiful. Every five pages gives a different description of time in an alternative universe. Some are familiar, others are strange or terrifying.

This is a good book for a lazy afternoon when you want to take your mind off of your typical troubles.

Book Review- Calico Joe

Calico Joe
by John Grisham, 2012

calico joe

In a departure from judicial dramas, John Grisham wrote a book about the personal effects of a beanball. Maria and I listened to this, on CD, last weekend, just in time for Spring Training. The story weaves a pair of fictional baseball players (one hero and one villain) into the real world of 1970’s baseball. The villain, motivated by a number of personal shortcomings and perceived slights, throws a beanball at the hero. The hero is hit in the face, and his promising but short career is ended. The story catches up with the characters 30 years after the incident to see if closure can be obtained.

While I appreciate baseball books and most of John Grisham’s stuff, this isn’t his best work by a long shot. A lot of the backstory on the narrator (the villain’s son) just makes you want to cringe, while the closure at the end seems like an odd combination of predictable and unrealistic.