Sports Analytics: Market Shares, Strategies, and Forecasts, Worldwide, 2015 to 2021

Published: May 2015
No. of Pages: 472
   

WinterGreen Research announces that it has published a new study Sports Analytics: Market Shares, Strategy, and Forecasts, Worldwide, 2015 to 2021. The 2015 study has 472 pages, 177 tables and figures. Worldwide markets are poised to achieve significant growth as the cloud computing for utility infrastructure and the tablets and smart phone communications systems make training information more cogent and more available, remaking all sporting everywhere.

Information services will leverage automated process to leverage cloud computing: services.The value of sports analytics is the predictive capabilities provided. The best sports teams are the ones using the power of real-time information to their advantage. What to measure? What real time information is the best? Can the players game the analytics systems?

Lets start with the story of Babe Ruth. The “Babe” used to come to every at bat with the desire to win the game. So early in the game, aware that at the end of the game it would fall on him to win the game, the “Babe” would deliberately strike out on pitches that he really could hit. Later in the game, the pitcher would remember the pitches that had gotten the “Babe” out and “Babe Ruth” could hit with ease, winning the game defying the statisticians.

So, Babe Ruth used sports analytics in the 1930’s in reverse, hoping to entice the pitcher to throw that very pitch he could hit in a tight situation later in the game. His very success illustrates that in sports analytics sophistication is needed. For sports analytics to track Babe Ruth, it would have been necessary to look at the pitches he could hit at the end of the game, not just everything that came at him. How sophisticated is that? You have to know your players to do good sports analytics.

Babe Ruth is at the center of one of the sad stories of sporting in Boston. The Boston Red Sox baseball team, in 2003, had not won a world series since Babe Ruth was sold to New York, the so called “Curse of the Bambino.” John Henry, a financial analytics wizard came along and purchased the Boston Red Sox along with other partners and he took the team to three world series using sports analytics as the dominant force for running the team and building fan enthusiasm.

Sports become the model for predictive business decision making. Business has been reorganized among teams, inspired by sports. Analytics, developed by businesses are finding innovative use in sports, leading to models for business to organize and manage teams.

Sports analytics market driving forces relate to the ability to improve winning percentages and decrease the cost of paying players. By implementing metrics functions that describe how to put together a winning team without a very high payroll, sports analytics provide a winning edge to team management. Analytics are used to figure out how a team can improve fan appeal.

Sports analytics are used for creating fantasy leagues, giving sports fantasy players access to statistics that enhances their play of the game. It is used to improve scouting, to detect new player unusual talent and evaluate players competitive capability. Using the system, the agent gains competitive advantage with teams when they present analysis about the players they represent.

Shift charts represent an image of changing data. In the chart above, the numbers along the top represent the shifts played during the game.. The black lines represent goals scored and show what line was on the ice offensively and defensively for each goal scored in each period, period one, period two, and period three.Sport analytics are about patterns, detecting patterns and attaching value to them by being able to predict better what players will succeed and what players will do well in a certain system. The patterns apply to teams, to players and to fans.

The data about the sport is relevant in a lot of different ways, some teams are more able than others to harness the patterns to their benefit. Does it make a difference? Do the teams with better analytics win? Apparently so. The MIT sports analytics conference is a testament to the value of technology in sports.

In hockey, analytics has been adopted big time, the trend this summer of 2015 has been for NHL clubs to hire bloggers and website operators so their content is proprietary.

Play of the Game is what makes sports entertainment, and the players entertainers. Hockey is a particularly appealing sport because it has so much player contact. It is a contact sport. Some of the better plyers play with finesse. Ovechkin for example, who had 27 even-strength goals this season (fifth in the league) and who scored a league-leading 24 power play goals is fun to watch. He is a premier player because of style and this makes him a fan favorite.

According to Susan Eustis, principal author of the market research study, “Sports teams have discovered that with intelligent use of sports analytics they can dominate a league. As the early adopters prove that analytics makes the difference between winning and losing, all teams, mangers, and fantasy sports players need to adopt use of the solutions creating market growth opportunities. .”

Sports analytics market size at $125 million in 2014 is anticipated to reach $4.7 billion by 2021. Significant growth is driven by the smart phone and social media in addition to cloud computing market penetration. With smart phones and tablets beginning to get significant uptake all over the world sports analytics play into that market expansion.

Growth is a result of sports league and team department efforts.

Companies Profiled

Market Leaders

  • Stats / Prozone
  • Perform / Opta
  • TruMedia
  • Catapult

Market Participants

  • Advanced Sports Analytics
  • Analytics Educational
  • Associated Press
  • Bodybuilding.com
  • Catapult: NHL Technology
  • Competitive Sports Analysis
  • IBM
  • Major League Baseball (MLB)
  • Teams
  • Motor Sports Analytics
  • National Football League (NFL)
  • Oracle
  • Perform / Opta Pro
  • Ramp Holdings
  • SmartSports
  • SmartSports / Sportvision
  • Sports Vision Technologies
  • Statistical Sports Consulting
  • Synergy Sports
  • TruMedia Networks
  • Vista Equity Partners / STATS

Sports Analytics: Market Shares, Strategies, and Forecasts, Worldwide, 2015 to 2021

Table of Contents

Sports Analytics Executive Summary 28
Sports Analytics Market Driving Forces 29
Sports Analytics Organizational Market Driving Forces 31
Play of the Game 39
Sports Analytics Market Shares 40
Sports Analytics Market Forecasts 41

1. Sports Analytics Market Description and Market Dynamics 43
1.1 All Teams Crunch Numbers 43
1.2 Sports Analytics That Appeal to the Fan Base 44
1.2.1 Hockey Analyses Take Into Account Situations (Even-Strength, Power Play, Shorthanded) 45
1.2.2 Analytics Change the Outcome of the Games 46
1.2.3 Seriously Flawed Sports Analytics 47
1.3 Team Sports Analytics 49
1.3.1 Red Sox Sports Analytics Information Services 49
1.3.2 Red Sox Win the World Series Three Times 52
1.3.3 Red Sox Value Patient Hitters 52
1.3.4 New York Yankees 53
1.3.5 Moneyball Is Alive And Well in Oakland 54
1.3.6 Oakland A's General Manager Billy Beane Moneyball 55
1.3.7 MLB Tampa Bay Rays 58
1.4 Hockey Analytics 59
1.5 Soccer Sports Analytics 62
1.5.1 Liverpool And The Director Of Football 62
1.5.2 Global Football Has Fundamental Shift Going On 63
1.6 NFL Stats Football Analytics 64
1.7 Media Sports Analytics 65
1.8 Auto Racing Stats and Analytics 65
1.9 Spurs of the National Basketball Association 66
1.9.1 NBA Corner Shot For Three Points 67
1.9.2 Resting Aging Stars For Deep Playoff Runs 67
1.9.3 NBA Rockets Team Investment in Analytics 67
1.9.4 Defensive Shifts In Baseball vs. Defensive Shifts in Hockey 68
1.10 MLB Tampa Bay Rays 70
1.11 Dallas Mavericks Basketball Team 71
1.12 NHL Hockey Los Angeles LA Kings 71
1.13 Professional Golfers 73
1.14 Road Cycling 73
1.15 Sports Data Visualization 75
1.15.1 Data Visualization 75
1.15.2 Sports Analytics for Fans 76
1.16 Sports Team Ownership 77

2. Sports Analytics Market Shares and Market Forecasts 80
2.1 Sports Analytics Market Driving Forces 81
2.1.1 Sports Analytics Organizational Market Driving Forces 83
2.1.2 Play of the Game 91
2.1.3 NHL Shift Charts 92
2.2 Sports Analytics Market Shares 96
2.2.1 Companies and Media Focused on Sports Analytics 97
2.2.2 Stats 104
2.2.3 Stats / Prozone Describes Performance 105
2.2.4 Perform / Opta 105
2.2.5 OptaPro Portal 106
2.2.6 TruMedia 106
2.2.7 Catapult Team 107
2.2.8 Catapult: National Hockey League NHL 107
2.2.9 Catapult Total Revenue 107
2.2.10 QSTC 108
2.2.11 Bodybuilding.com 108
2.2.12 Sportvision 108
2.2.13 Fox NFL Predictions 110
2.2.14 Synergy Basketball Designed for Coaches By Coaches 110
2.3 Sports Analytics Market Forecasts 111
2.3.1 Sports Analytics Market Segments 113
2.3.2 Personal Analytics 117
2.4 Sports Analytics Regional Market Analysis 118
2.4.1 US 119

3. Sports Analytics Product Description 120
3.1 STATS 120
3.1.1 Stats’ SportVU Technology 120
3.1.2 Stats ICE - Basketball Operations Solutions 123
3.1.3 Stats Fantasy Sports 123
3.1.4 Stats Fantasy Games 126
3.1.5 Stats Pick / Predictor Fantasy 126
3.1.6 Stats Salary Cap Fantasy 127
3.1.7 Stats Leagure Stype Fantasy 127
3.1.8 Stats Commissioner Fantasy 127
3.1.9 Stats Bracket Fantasy 128
3.1.10 Stats' Sports Solutions Group 128
3.1.11 Stats Player Tracking 129
3.1.12 STATS MatchCast 130
3.1.13 Stats Prozone 134
3.1.14 Prozone World Cup 2014 134
3.1.15 Prozone Data, Information, Insights. 135
3.1.16 Prozone’s Football Heritage 135
3.1.17 Prozone Describes Performance 137
3.1.18 Prozone Opposition Scouting 137
3.1.19 Prozone Team Analysis 138
3.1.20 Prozone Physiological Monitoring 138
3.1.21 Prozone Player Recruitment 140
3.1.22 Stats Global Network 141
3.1.23 Stats Strategic Support 141
3.1.24 Stats Case Studies 142
3.1.25 Stats Football 143
3.1.26 Stats Rugby Union 147
3.1.27 Stats Rugby League 148
3.2 Perform / OptaPro 148
3.2.1 OptaPro VideoHub Elite 149
3.2.2 OptaPro VideoHub Elite Competitions Covered 153
3.2.3 OptaPro Portal 154
3.2.4 Opta 155
3.2.5 Opta Sports Data 156
3.2.6 Opta Sportsbook Predictive Analytics & Data Modelling 156
3.2.7 Opta Analytics In Action 156
3.3 TruMedia Sports Analytics 160
3.3.1 TruMedia's MLB Analytics Platform 161
3.3.2 TruMedia MiLB Minor League Analytics 162
3.3.3 TruMedia Soccer Analytics 166
3.3.4 TruMedia Crossing Pattern Football Analytics Platform / ESPN 168
3.4 Sportvision 170
3.4.1 Sportvision Motorsports Driving Innovation 170
3.4.2 ESPN Commits to Sportvision K-Zone Live on Every Pitch for MLB Coverage 173
3.4.3 SmartSports, Boston-Based Parent Company of SmartKage, and Sportvision 174
3.4.4 NHL, Sportvision Progress in Chip-Based Player Tracking 175
3.4.5 NHL Website Advanced Statistics 179
3.5 Sports Vision Technologies P3ProSwing Professional Golfers 180
3.6 Fox What If Embraces Technology as It Redefines Sports Competition 182
3.6.1 Fox What If Sports Simulations 183
3.6.2 Fox Sports Analytics 184
3.6.3 STATS LLC Global Sports Statistics 185
3.6.4 Stats Quarterbacks 185
3.4.5 Stats Running Backs 187
3.4.6 Stats Tackle 187
3.6.7 FoxSports.com 188
3.6.8 Foxsports.com / Whatifsports.com 189
3.6.9 FoxSports.com WhatIfSports.com: Positioned As Sports Simulation Destination 190
3.6.10 Foxsports NFL Prediction Widgets 190
3.6.11 Foxsports CFB Predictions 190
3.6.12 Foxsports SimMatchup 191
3.6.13 Foxsports MLB Power Rankings 192
3.7 ESPN Analytics 195
3.7.1 ESPN NFL National Football League 195
3.7.2 ESPN Major League Baseball Sports Analytics 197
3.7.3 National Basketball Association 199
3.7.4 ESPN Blackhawks Hockey Analytics Effectiveness 201
3.7.5 ESPN Stats & Information 204
3.7.6 ESPN Stats & Info 205
3.8 CBS Sports Analytics 205
3.8.1 St. Louis Blues Coach Ken Hitchcock Uses Analytics To Help Make Better Line Combinations 207
3.8.2 NHL Shot Location Data 210
3.9 Cognitive Computing Real Time Sports Analytics 211
3.10 Pro Football Focus 213
3.10.1 SportVU Football Solutions 213
3.11 IBM Watson Cognitive Computing 214
3.11.1 IBM Golf TryTracker 215
3.11.2 IBM Grand Slam Tennis 219
3.12 Sports Analytics Institute: Player Evaluation System 223
3.12.1 Sports Analytics Institute Growing an Organization's Sports Analytics Competency 225
3.12.2 Sports Analytics Institute: Hockey 226
3.13 Baseball Swing Analysis 227
3.13.1 MLB myHits 6 Key Hitting Stages 236
3.13.2 MLB League Tools and Services 243
3.14 82games 244
3.15 Catapult 248
3.15.1 Catapult Team Customer Base 249
3.15.2 Catapult Monitoring Elite Athletes 300
3.16 Real Sports Analytics 305
3.16.1 Real Sports Analytics Player Performance Scorecards 306
3.17 Sports Business Intelligence 310
3.18 SAS 311
3.18.1 SAS Sports Analytics 312
3.18.2 SAS Customer Intelligence Analytics 312
3.19 SAP 313
3.20 Hawk-eye 313
3.21 Nike+ 315
3.21.1 Nike Personal Analytics 315
3.22 QSTC 316
3.23 Synergy Sports 316
3.24 CSA – Competitive Sports Analysis 317
3.25 Sports Analytics Institute 318
3.25.1 Sports Analytics Institute Player Evaluation System 318
3.26 Oracle 320
3.27 Google Analytics 320
3.27.1 Google Analytics Used In Loyalty Program 321

4. Sports Analytics Technology 322
4.1 Legislation: UEFA’s Financial Fair Play (FFP), Premier League’s Elite Player Performance Plan (EPPP) 322
4.1.1 UEFA’s Financial Fair Play (FFP) 322
4.1.2 Elite Player Performance Plan (EPPP) 324
4.1.3 Elite Player Performance Plan (EPPP) Focus on Youth Development 328
4.2 Major League Baseball MLB Analytics 329
4.3 US National Football League NFL 331
4.4 John Henry Owner of Boston Red Sox Uses Sports Analytics to Win World Series 332
4.5 Stats Technology 333
4.5.1 STATS Servers 335
4.5.2 STATS RESTful API 337
4.5.3 Stats Interactive 347
4.6 Sports Analytics Dynamic Architecture 349
4.6.1 Google Search Engine Dynamic Architecture 351
4.6.2 BigFiles 352
4.6.3 Repository 352
4.6.4 Microsoft .Net Defines Reusable Modules Dynamically 353
4.6.5 Microsoft Combines Managed Modules into Assemblies 354
4.6.6 Microsoft Architecture Dynamic Modular Processing 354
4.6.7 IBM SOA Architecture is Dynamic for the Transport Layer 357
4.7 IBM WebSphere MQ Dynamic Architecture is Base for SOA 362
4.8 IBM Software Enterprise Service Bus 363
4.8.1 IBM ESB and SOA 364
4.9 Electromagnetic 12 Sensor 6 DOF Golf System 364
4.8.2 Golf Electromagnetic Flexible Screen 367
4.8.3 Experts Can Note Needed Improvements, Create Database Of A Person’s Own Swings 367

5. Sports Analytics Company Profiles 369
5.1 Advanced Sports Analytics 370
5.2 Analytics Educational 370
5.3 Associated Press 370
5.3.1 AP Positioning 371
5.3.2 Associated Press Not-For-Profit Cooperative 373
5.4 Bodybuilding.com 376
5.5 Catapult: NHL Technology Reduces Injuries 377
5.5.1 Catapult Focused on US College Sports System 378
5.5.2 Catapult Data Collection 379
5.5.3 Catapult Revenue 379
5.5.4 Catapult Regional Revenue 380
5.5.5 Catapult Total Revenue 381
5.5.6 Catapult US: 381
5.5.7 Catapult EU 381
5.5.8 Catapult ROW 381
5.5.9 Catapult Total Units Ordered 381
5.5.10 Catapult Player Tracking in Australian Rules Football 383
5.5.11 Catapult Hockey Player Tracking 383
5.5.12 Catapult Device 384
5.5.13 Catapult in the NFL 386
5.5.14 Catapult Can Help Trainers Understand How Much Stress Of The Game 387
5.5.15 Catapult Measuring Intense Play 388
5.5.16 Big Wave Surfers Use Catapult to Ready for Event 390
5.6 Competitive Sports Analysis 390
5.7 Major League Baseball (MLB) Teams 391
5.7.1 MLB.com Digital Academy Instructional Center 392
5.7.2 MLB Coaches Corner 393
5.7.3 Youth Baseball Leagues 393
5.7.4 MLB my Hits® 394
5.7.5 MLB myPitch 395
5.8 Motor Sports Analytics 396
5.9 National Football League (NFL) 396
5.9.1 AFC-North 397
5.9.2 AFC-South 397
5.9.3 AFC-East 398
5.9.4 AFC-West 398
5.9.5 NFC-North 399
5.9.6 NFC-South 400
5.9.7 NFC-East 400
5.9.8 NFC-West 401
5.9.9 NFL Stats 402
5.10 Perform / Opta Pro 403
5.10.1 Opta 404
5.10.2 Opta Partner Clients 405
5.10.3 Opta Partners for Betting 405
5.10.4 Opta Partners for Broadcast 406
5.10.5 Opta Partners for Online and Mobil 407
5.10.6 Opta Partners for Print 415
5.10.7 Perform Revenue 420
5.10.8 Perform Acquires Opta 422
5.11 Ramp Holdings 423
5.11.1 RAMP Holdings ROI 423
5.11.2 RAMP Holdings Capital Investment and Revenue 423
5.11.3 RAMP Holdings Partners 425
5.12 SmartSports 426
5.12.1 SmartSports / Sportvision 427
5.12.2 Sportvision 429
5.12.3 MLS Teams Seek Edge With Player-Tracking Technology 431
5.13 Sports Vision Technologies 431
5.14 Statistical Sports Consulting 432
5.15 Synergy Sports 432
5.15.1 Synergy Basketball Designed for Coaches By Coaches 433
5.15.2 Synergy Changes The Game 433
5.16 TruMedia Networks 435
5.16.1 Tony Khan Acquires Sports Analytics Firm TruMedia Networks 436
5.16.2 TruMedia Networks / Detroit Tigers Long Term Licensing Agreement 436
5.16.3 TruMedia Partners with Harvard Sports Analysis Collective 438
5.16.4 Jacksonville Jaguars Executive Tony Khan makes Strategic Investment in TruMedia Networks 439
5.16.5 TruMedia Networks Baseball Analytics Site In Partnership With Journalist Peter Gammons 439
5.16.6 TruMedia Networks and ESPN Power NFL Crossing Pattern Analytics Product 439
5.17 Vista Equity Partners 441
5.18.1 STATS 441
5.17.2 Stats Was Part of News Corporation (the parent of FOXSports.com) and the Associated Press 442
5.17.3 Stats Customers 445
5.17.4 STATS / Prozone 447
5.17.5 Prozone Software Tracks In-Game Player Performance 450
5.17.6 Stats Revenue 451
5.17.7 Stats Locations Worldwide 451
5.17.8 STATS Sports Public Relations 452
5.17.9 STATS Data And Content Company 453
5.17.10 Stats Data Centers 453
5.17.11 Stats Acquisitions 454
5.17.12 STATS / Sportz Interactive 455
5.17.13 STATS Projections for Daily Fantasy Sports 456
5.17.14 Vista Equity Partners And STATS Acquire Automated Insights 456
5.17.15 STATS Acquires The Sports Network 456
5.17.16 STATS Acquires TVTI 456
5.17.17 STATS Acquires Bloomberg Sports 457
5.17.18 STATS / Automated Insights 458
5.18 Sports Analytics Companies 458
5.18.1 Sports Analytics Vendors 459
5.18.2 PRINT MEDIA 461
5.18.3 DIGITAL MEDIA 463
5.18.4 Television/Video 465

List of Tables and Figures

Table ES-1 Types of Organizations Using Sports Analytics 30
Table ES-2 Sports Analytics Market Driving Forces 32
Table ES-3 Sports Analytics Market Driving Factors for Player’s Agents 33
Table ES-4 Sports Analytics Market Aspects 34
Table ES-5 Sports Analytics Market Forces 35
Table ES-6 Sports Video Analytics Market Driving Forces 36
Table ES-7 Sports Analytics Fantasy Game Market Driving Forces 37
Table ES-8 Sports Analytics Uses 38
Figure ES-9 Sidney Crosby #87 Of The Pittsburgh Penguins Celebrates A Second Period Goal With Teammate 39
Figure ES-10 Sports Analytics Market Shares, Dollars, Worldwide, 2014 40
Figure ES-11 Sports Analytics Market Forecasts Dollars, Worldwide, 2015-2021 42
Figure 1-1 Hockey Goal Scoring 46
Table 1-2 Owner John Henry and the Red Sox Leverage Sports Analytics 50
Table 1-3 Red Sox Sports Analytics Positioning 51
Figure 1-4 Red Sox Value Patient Hitters 53
Table 1-5 Sports Analytics in the Context of Physicality 57
Figure 1-6 Major League Baseball Average Roster Cost Per Win 59
Table 1-7 Web Sites Dedicated To Hockey Analytics 60
Figure 1-8 Rockets Lowest Percentage Of Midrange Shots 68
Figure 1-9 Major League Baseball Average Roster Cost Per Win 70
Figure 1-10 NHL Hockey Los Angeles LA Kings 72
Table 1-11 Cycling Computer Output 74
Table 1-12 Factors that Impact Ownership Use of Analytics for Sports Management 79
Table 2-1 Types of Organizations Using Sports Analytics 82
Table 2-2 Sports Analytics Market Driving Forces 84
Table 2-3 Sports Analytics Market Driving Factors for Player’s Agents 85
Table 2-4 Sports Analytics Market Aspects 86
Table 2-5 Sports Analytics Market Forces 87
Table 2-6 Sports Video Analytics Market Driving Forces 88
Table 2-7 Sports Analytics Fantasy Game Market Driving Forces 89
Table 2-8 Sports Analytics Uses 90
Figure 2-9 Sidney Crosby #87 Of The Pittsburgh Penguins Celebrates A Second Period Goal With Teammate 91
Figure 2-10 NHL Shift Chart Player Statistics 92
Figure 2-11 NHL Shift Chart Goals Scored Line Statistics 93
Figure 2-12 NHL Entire Game Shift Chart 95
Figure 2-13 Sports Analytics Market Shares, Dollars, Worldwide, 2014 96
Table 2-14 Sports Analytics Market Shares, Dollars, Worldwide, 2014 97
Figure 2-15 MIT Sloan Sports Analytics Conference Attendees 98
Table 2-16 MIT Sloan Sports Analytics Conference Attendees 99
Table 2-17 Media Using Sports Analytics 100
Table 2-18 Digital Media Using Sports Analytics 102
Table 2-19 Television / Video Media Using Sports Analytics 104
Figure 2-20 Sports Analytics Market Forecasts Dollars, Worldwide, 2015-2021 112
Table 2-21 Sports Analytics Market Forecasts Dollars, Worldwide, 2015-2021 113
Table 2-22 Sports Analytics Market Segments, Worldwide, Dollars, 2015-2021 114
Figure 2-23 Sports Analytics Market Segments, Worldwide, Percent, 2015-2021 115
Table 2-24 Sports Analytics Technology Target Markets 116
Figure 2-25 Sports Analytics Regional Market Segments, Dollars, 2014 118
Table 2-26 Sports Analytics Regional Market Segments, 2014 119
Figure 3-1 Stats’ SportVU Technology 121
Table 3-2 STATS’ SportVU Technology Target Markets 122
Table 3-3 Stats Turn-Key Fantasy Solution Functions: 124
Figure 3-5 Stats Fan Experience 129
Table 3-6 Stats Leveraging The Timeline 133
Figure 3-7 Opta Sport Analytics Advanced Layer, Next Level Of Data Provision 149
Figure 3-8 Opta VideoHub Elite Data-Led Video Analysis 151
Table 3-9 Opta VideoHub’s Key Strengths 152
Figure 3-10 OptaPro VideoHub Elite Competitions Covered 153
Figure 3-11 OptaPro Portal 155
Figure 3-12 Opta Cricket Wagon Wheel Graphic, Created Using Data For BBC Sport 157
Figure 3-13 Opta Analytics Charting Success, Unsuccessful, and Assists 158
Figure 3-14 Investec Leveraging Opta Data Analytics 159
Figure 3-15 TruMedia's MLB Analytics Platform 161
Figure 3-16 TruMedia Networks Albert Pujols Batting Pattern 162
Table 3-17 TruMedia Analytics Platform Positioning 163
Figure 3-18 TruMedia Heat Zone Analytics 164
Figure 3-19 TruMedia Soccer 166
Table 3-20 TruMedia's Soccer Analytics Platform League Coverage 167
Figure 3-21 ESPN uses TruMedia's Soccer Analytics Platform 168
Table 3-22 TruMedia / ESPN Crossing Pattern NCAA Conferences Covered: 169
Figure 3-23 Sportvision Sports Tracked 172
Figure 3-24 Sportvision NHL Puck Tracking System 176
Figure 3-25 Sportvision NHL Game Tracking System 178
Figure 3-26 Sports Vision Technologies P3ProSwing In-depth Golf Swing Analysis 180
Table 3-29 Golf Courses Available on P3ProSwing Golf Analytics Simulator 181
Table 3-30 Fox Sports Analytics Types of Simulations 184
Figure 3-31 Foxsports Dream Team SimMatchup 192
Table 3-32 Foxsports Whatifsports.com 194
Table 3-33 ESPN NFL Top 10 Analytics Use Ranking 195
Table 3-34 ESPN Major League Baseball MLB Analytics Use Ranking 197
Table 3-35 ESPN National Basketball Association NBA Analytics Use Ranking 199
Table 3-36 ESPN NHL National Hockey League Analytics Use Ranking 202
Table 3-37 ESPN Insider Knowledge Blog Posts 204
Figure 3-38 Hockey Analytics To Help Make Better Line Combinations 207
Table 3-39 Analytics Use as a Coaching Tool 209
Table 3-40 NHL Team Activities That Depend On Analytics 210
Table 3-41 Cognitive Computing Real Time Sports Analytics 211
Table 3-42 Cognitive Computing Sports Analytics Functions 212
Figure 3-43 IBM Augusta National Golf Try Tracker 217
Figure 3-44 IBM Predictive Analytics Technology Used In Rugby 218
Figure 3-45 IBM Sports Analytics Tennis Slam Tracker 220
Figure 3-46 IBM Sports Analytics Player Tracker 221
Figure 3-47 IBM Sports Analytics Tennis Stats COmparisons 221
Figure 3-48 IBM Sports Analytics Tennis Set Comparisons 222
Figure 3-49 IBM Sports Analytics Tennis Keys to the Match Tracker 222
Figure 3-50 Sports Analytics Institute Player Lifetime Value Evaluation System Components 223
Table 3-51 Sports Analytics Institute Player Evaluation System Stages 224
Figure 3-52 Major League Baseball MLB Baseball Swing Analysis 227
Figure 3-53 6 Key Hitting Stages 237
Table 3-54 Baseball Key Hitting Stages 238
Figure 3-55 Teaching Young Players Analytics 239
Figure 3-56 MLB Hitting Analytics for Young Players, Comparison to Big League Hitting Stars 241
Table 3-57 MLB.com Digital Academy Youth League Management Tools And Instructional Resources 244
Table 3-58 82games Types of Basketball Numbers 245
Table 3-59 82games Stats Collected on Each Player in a Game 246
Table 3-60 Catapult Team Customer Base 250
Table 3-61 Catapult for Coaches Providing Scientifically-Validated Metrics on Athlete Performance 301
Figure 3-62 Real Sports Analytics Player Performance Scorecard 306
Figure 3-63 Real Sports Analytics Player Detail View 307
Figure 3-64 Real Sports Analytics Player Weekly Performance Scorecard 308
Table 3-65 Real Sports Analytics Game Metric Player Measure 309
Figure 3-66 Real Sports Analytics Player Color Coded Performance Scorecard 310
Table 3-67 SAS Sports Analytics Functions 312
Table 3-68 Hawk-eye Sports Analytics Features 314
Table 3-69 Sports Analytics Institute Player Evaluation System Features 318
Table 3-70 Google Analytics Used In Loyalty Program 321
Table 4-1 UEFA’s Financial Fair Play (FFP) 323
Table 4-2 Elite Player Performance Plan (EPPP) Fundamental Principles: 325
Table 4-3 Elite Player Performance Plan (EPPP) Focus Areas 326
Table 4-4 Elite Player Performance Plan (EPPP) Grading Factors 327
Table 4-5 Key Areas of EPPP Focus 329
Table 4-6 Major League Baseball MLB Streaming Media Analytics Functions 330
Figure 4-7 Stats Data Center Technology 333
Table 4-8 STATS Data Delivery Protocols: 334
Table 4-9 STATS Servers Modules 335
Figure 4-10 Stats Content Delivery 336
Figure 4-11 Oracle Powers Stats Databases 336
Figure 4-12 Stats Secure Connection 338
Table 4-13 Stats Information Provided 339
Table 4-14 Stats Sports Covered 340
Table 4-15 Stats Sports Leagues Covered 341
Table 4-16 Stats Interactive Functionality 348
Table 4-17 Google Dynamic Architecture 349
Figure 4-18 Microsoft .Net Dynamic Definition of Reusable Modules 353
Figure 4-19 Microsoft .NET Compiling Source Code into Managed Assemblies 355
Figure 4-20 Microsoft Architecture Dynamic Modular Processing 356
Table 4-21 Process Of SOA Implementation Depends On N-Dimensional Interaction Of Layers That Can Be Modeled by Business Analyst 358
Table 4-22 IBM SOA Business I Services Layers 359
Figure 4-23 IBM Smart SOA Continuum 360
Table 4-24 SOA Foundation Reference Architecture 361
Figure 4-25 IBM WebSphere MQ WMQ Providing a Universal Messaging Backbone 362
Figure 4-26 Golf Swing Analyzer 366
Table 4-27 Golf Biomechanics Report Features: 368
Table 5-1 Motion Measurement Analysis Functions 369
Figure 5-2 AP Global Reach Statistics 372
Figure 5-3 AP Image Statistics 373
Figure 5-4 AP Revenue By Customer and Format 374
Figure 5-5 AP Download Statistics 375
Figure 5-6 AP Growth in Sales 375
Figure 5-7 AP Newsroom Profile 376
Table 5-8 Catapult System Device Description and Components 384
Table 5-9 Catapult System Device Positioning 385
Table 5-10 Catapult System Device Functions 385
Figure 5-11 Catapult Trending on The Daily Cut 386
Figure 5-12 Catapult Trending on The MLB Stress 387
Table 5-13 Motor Sports Analytics Features 396
Figure 5-14 Opta Partners for Betting 405
Figure 5-15 Opta Partners for Broadcast 406
Figure 5-16 Opta Partners for Online and Mobil 407
Figure 5-17 Opta Partners for Clubs and Governing Bodies 409
Figure 5-18 Opta Partners for Print 415
Figure 5-19 Opta Sponsors and Brands 417
Figure 5-20 Opta Partners 418
Figure 5-21 RAMP Holdings Investors 424
Figure 5-22 RAMP Holdings Integration Partners 425
Figure 5-23 RAMP Holdings Technology Partners 426
Table 5-24 Sportvision Credentials: Sports Broadcasting Technology 430
Table 5-25 TruMedia Networks Platform Components 437
Table 5-26 TruMedia Networks Analytics Solutions Target Markets 438
Figure 5-27 Stats Companies 443
Table 5-28 STATS Sports Technology Target Markets 444
Figure 5-29 Stats Customers 445
Figure 5-30 Prozone Cameras 448
Table 5-31 Prozone Optical Player Tracking 449

Published By: WinterGreen Research
Product Code: WinterGreen Research1113


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