These are interactive tables, graphs and charts that allow you to slice the data quickly and make your research process even easier. These visuals will replace the traditional MLB Game Logs and Player Scoring Calendar while adding more tools that I could only dream of in the past. These will continue to be Pro Tools and I believe will add incredible value to the price of your membership. Below is a tutorial to show the new MLB Visuals and a few FAQs. Finally, these tools are FOR YOU. I want to make these as awesome as possible, so please send me all your suggestions and feedback. I can be reached on Twitter or via email.
Here are my MLB Projections! Below you will find both the online and downloadable versions of my MLB Projections. These are version 3.0 and have come through many changes and upgrades since the 2016 season. You can see some of the bigger updates here.
These projections take will take into account three main factors: recent performance, matchup and ballpark factor. Those three factors lend themselves very well to fantasy scoring.
Stacks Tab – *New for 2017* – As lineups roll in, this will calculate the projections for that group of players as a whole. Broken down by hitters 1-4, 5-9 and 1-9 (all hitters).
BO – *New for 2017* – The position in the batting order for that hitter. Updates live as lineups are announced.
2x, 3x, 4x – The amount of fantasy points a player needs to score to reach 2x, 3x and 4x value on his salary. PitchWeight – [For Hitters] The amount of fantasy points an opposing pitcher allows with 1 being the league average. (1.2 means they allow 1.2x the league average). OppWeight – [For Pitchers] The amount of fantasy points an opposing lineup allows with 1 being the league average. (1.2 means they allow 1.2x the league average). Park – The amount of fantasy points the home ballpark allows compared to the league average. %2x, %3x and %4x – The likelihood that a player reaches those value milestones. Mean – Average fantasy points scored by that player. StdDev – The mathematical calculation for standard deviation.
The PGA Game Logs Database is exactly what it sounds like, it’s every game log for every player who has played in a PGA Tournament this year. It can be used to help identify trends, create projections or be the source data for countless models. It includes all the standings information, DraftKings scoring/salaries, Vegas lines and a handful of key stats on driving, greens hit and putting.
I am trying to make this as easy as possible for you to use. So this will be updated regularly in a few versions:
Predicting the outcomes of games and players is hard enough. When a matchup is predicted to lopsided, it can make the task of a DFS player even more difficult. Obviously you want to target players that are on good teams in situations to win big, but if the game gets completely out of hand, you could lose your best players who might ride the pine down the stretch. I have found success in targeting players that I deem “blowout proof” in these games. I made a video about it:
Be sure to check out our other posts and videos for more great DFS strategy! Follow us on Twitter for up to date player analysis. Good luck!
Most players realize that they should be using different strategies for the type of contest they are playing. Traditionally, you want to choose safe or guaranteed points in cash games where you will need a much lower score to beat your one opponent or finish in the top half of the field. On the other hand, to win a GPP you will need a lot more points, so it’s in your benefit to choose players who are more volatile (aka risky). They may put up a dud, but when they go nuts you will have a much better chance of winning the GPP. You should be willing to take on greater risk, because of the significantly larger reward.
I created the “Players Scoring Calendar” and one of the best benefits is to see how consistent a player actually is. The “Standard Deviation” column shows just that. The lower the standard deviation, the more consistent a player is on nightly basis. Those players are great cash game options. Players who boast a higher standard deviation are more volatile and suited for GPPs. It’s an excellent tool and I show how to use it below:
This video shows five simple ways to go against the grain and be contrarian in the NBA. As you get more experienced, you’ll be able to predict ownership levels for many players. One common theory in GPPs is to favor players who you think will have low ownership. If they have big games, you will set yourself apart and shoot up the leaderboards. Topics covered in this video include dissection pricing, matchups, recent performance and more! Enjoy this video on How To Be Contrarian in the NBA.