2017 KillerSports.com Journal of MLB Research Featuring the SDQL MLB Studies from Handicapping and Data Experts 580 Perfect Team and Starter Trends and Much, Much More The 2017 KillerSports.com Journal of MLB Research Introduction ........................................................................................................................................... 4 SDQL Overview ...................................................................................................................................... 5 Team Records vs Player Hits, By Dr Edwin F Meyer, KillerSports.com ................................................6-7 9th Inning Momentum Study, By Vince Akins, SportsBook Breakers .................................................8-9 Shutout MLB System, By Mr East. .......................................................................................................10 Series Openers, By MTi Sports Forecasting. ...................................................................................12-15 MLB Scatter Plots, By Joe Meyer, SportsDataBase.com. .....................................................................16 After a Complete Game Shutout, By John Currey, SDQL Master ....................................................18-19 Featured Starter Trend: Cole Hamels, Sports Data Query Group ........................................................21 40 Starter Trends, By Vince Akins, SportsBook Breakers ................................................................22-24 More MLB Scatter Plots, By Joe Meyer, SportsDataBase.com. .......................................................26-28 In-Game Wagering, Sports Data Query Group. ..............................................................................30-31 Killersports.com MLB Trends Set Introduction ....................................................................................33 Complete Team-By-Team Trends and Data Pages ...........................................................................34-93 2 • KillerSports.com Coming April 2017 Killersports.com, MTi Sports and SportsBook Breakers are teaming up to release a brand new DAILY MLB Newsletter This new MLB Newsletter will include trends, data analysis and key schedule chart breakdowns made possible by SDQL and exclusive to this newsletter. Visit Killersports.com this April and all MLB season for more information. 2017 KillerSports.com Journal of MLB Research • 3 INTRODUCTION The 2017 KillerSports.com Journal of MLB Research more information, check out this baseball specific manual contains information that was uncovered using the on the SDQL: revolutionary Sports Data Query Language (SDQL). The http://killersports.com/Download/MLB/query_manual. SDQL allows fast, reliable and unprecedented access to pdf historical sports data over the internet from anywhere on the planet. If you are looking for a more concise guide to the subtle complexities of SDQL covering all sports, visit This Journal contains several research papers written by the guide at: certified SDQL masters as well as two pages of stats, info and performance indicators for each MLB team. http://www.sdql.com/intro.html The main set of performance indicators was uncovered We have no doubt that you will quickly become by the Sports Data Query Group. This team offers their hooked on the SDQL at Killersports.com and the quality complete active trend set in major league baseball daily at: information that the handicappers at KillerCappers.com can provide with it. http://killersports.com/trend_mart There are also a couple of great ways to track the trends Every day, there are hundreds of performance indicators found in this book. The first is to save these trends (or ones to look through and anyone that purchases access is you discover on your own) at Killersports.com. To do this welcome to save them to their personal trend set at you need to be logged in as a member. After performing a KillerSports.com. query (from the NBA or the MLB or the NFL query page), The set of perofrmance indicators in this Journal you will see a “save to personal trends” hyper link above contains eighteen trends in past performance for each the query text input box. team. There are six moneyline trends, six run-line trends, Clicking on this save to personal trends link will bring and six OU trends for each team. Of the six, there are you to a page where you can type in a description of the three of each flavor (on-against or over-under). trend. Along with a description, many like to type in the ALL 480 of these trends come with the Sports Data record of the trend here in order to track the performance Query Language (SDQL) that generates the trend. With throughout the season. the SDQL text you can verify the accuracy of the trend, but After entering a description clicking on the SAVE more importantly you can see how the trend is performing button saves your trend. When one of your trends is anytime during the season. To check any trend, type (or active, you will see a hyperlink to the game listing and paste) the SDQL text into the query text box at: your descriptive text on the My Trends page. http://killersports.com/mlb.py/query For a short video on how to save MLB trends, go here: and then simply click on the query button. The results https://www.youtube.com/watch?v=Wz3L_xYxD1w should appear in less than a second. Currently, you are allowed two free trends, after which If you have any questions about the SDQL, post them each trend costs two dollars per season. at the Google Group for the SDQL at: KillerSports.com would like to thank MTi Sports, http://groups.google.com/group/SportsDataBase SportsBook Breakers, the Sports Data Query Group and The group has many expert members, including the everyone else involved for their contributions to this genius behind the Sports Data Query Language, Joe Journal and hope you enjoy the content and find it useful Meyer. Feel free to post any questions/comments about during the baseball season. this publication there as well. Join us all season long and let’s make this a successful For self-starters, check out the one-page basics on how 2017 campaign. to use the SDQL on the next page. If you are looking for 4 • KillerSports.com SDQL INTRODUCTION SDQL stands for Sports Data Query Language. It is a opponent. For example, we can see how a team performs language that allows the investigation of past sports re- when they score at least five runs and we can see how a sults over the internet using your home computer. It is team performs when their opponent scores at least five easy, it is fast and it is free. You do NOT have to know runs. how to program a computer to use the SDQL. If you can perform a search on Google, you can query the past results For example, to see how the Giants have performed in of professional sports games. games in which they scored at least five runs, use: Like the Google search, there is a text query box in which team = Giants and runs >= 5 you enter what you would like to search. Unlike Google, query the search has to be specific and you must use the Sports Data Query Language. The advantage of the SDQL is When this query is run, the computer responds with a re- that you get exactly what you ask for. cords summary and a game listing of all the games since 2004 in which the Giants scored at least five runs. For example, if you want to see all the no-hitters in major league baseball since 2004 simply enter: Since there is no game reference on the parameter ‘runs’ it refers to the team and the game in question. To see how the Giants have performed in games in which their hits=0 opponent scored at least five runs, use: query into the query text box and then click on the query button. team = Giants and o:runs >= 5 It is as simple as that! The web address of the MLB query query page at KillerSports.com is: The o: prefix on the “runs” points the runs parameter to http://killersports.com/mlb.py/query the opponent. To see all the MLB games in which a team scored at least To see how the Giants have performed in games AFTER 10 runs without hitting a home run, use the SDQL: they scored at least five runs, use: HR=0 and runs>=10 team = Giants and p:runs >= 5 query query and then click on the query button. That’s it! Here, the p: prefix on the “runs” points the runs parameter to the team’s previous game. The SDQL allows access to billions of situations that are of interest to sports historians, the sports media, fantasy Each one of these queries has two SDQL phrases. The league participants and serious sports bettors. first defines the team and the second gives a condition. There is no limit to the number of SDQL phrases that can An ability to quickly and efficiently interrogate historical be strung together with the word “and.” data in Major League Baseball (as well as the NBA and NFL) will provide the SDQL user a terrific advantage That’s it. This is the basic structure of the SDQL. This over those that just pore over box scores and read other structure will allow the thorough interrogation and in- people’s interpretations of the results. vestigation of historical sports data. Understanding this structure is the key to understanding the SDQL. Once you Perhaps the best way to grasp the SDQL is to simply try have a grasp of this structure, you will be able to perform the hundreds of examples in this book. That said, there your own investigations. are only a couple of key ideas that will get you well on your way to becoming an SDQL master. Start by trying the many examples in this book. If you have any questions about the SDQL, address them to the The first is that a query consists on a number of conditions sportsdatabase.com discussion group at: separated by the word “and.” The second is grasping the difference between the team and the opponent. In sports, http://groups.google.com/group/SportsDataBase there are two combatants. To distinguish between them, SDQL calls one of these the team and the other the op- This group is monitored by numerous SDQL masters ponent. This allows access to results based on both the who will be able to answer all your questions.