Great choices from Contrarian Edge Optimizer
An optimizer is a powerful tool to harness in DFS play, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. Like any other tool, one must first learn how to wield an optimizer before its true power can be realized. That is exactly what we will look to sort through in this weekly series. We’ll focus on Contrarian Edge Optimizer use at Fantasy Sports Logic for the Monday slates each week of the 2023 season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the newest, and most dynamic, optimizer in the industry.
As we typically do, running the optimizer without manipulating anything will give us a good idea of what to expect from the field tonight. The blended roster appears to be focused on fitting both quarterbacks in, returning a build of Hunter Renfrow at captain, Jared Goff, Jahmyr Gibbs, Amon-Ra St. Brown, Jimmy Garoppolo, and Josh Jacobs. There is a ton to discuss based off that first run alone.
First off, let’s break down the returned roster from the optimizer before getting into the rest of the theory for Week 8 Monday Night Football.
Hunter Renfrow carries a low 5.1 percent team target market share and 9.9 targets per route run rate for the Raiders this season as he has been largely phased out of the offense (and appears likely to be on his way out of town before the trade deadline). That profile would likely require a deep average depth of target (aDOT) to return viable production for the captain slot. Except Renfrow holds a modest 8.8 aDOT in this offense this season. That means Renfrow should be reserved for flex usage on this slate, and even then, his chances of cracking the optimal roster are extremely thin.
50.6 percent of the available targets for the Raiders have flowed through Davante Adams and Jakobi Meyers this season. 84.0 percent of the team’s backfield opportunities have gone to Josh Jacobs. That, my friends, is an extremely concentrated offense. Even so, it remains highly unlikely all three see the requisite production on a showdown slate to simply force them onto rosters together, leaving us with our first general rule for this evening:
At least one of, and no more than two of, Davante Adams, Jakobi Meyers, and Josh Jacobs.
Because the concentration of work is so tight amongst those three Raiders players, it is not required to play them paired with their quarterback, Jimmy Garoppolo. In fact, Garoppolo has just one game all season with more than a modest 16.1 fantasy points on an offense that has struggled in the red zone to the tune of a 27th-ranked 41.67 percent red zone touchdown rate (the rate of red zone trips that result in a touchdown). I conducted a study this offseason that attempted to find the correlation between touchdowns and fantasy production at the four major positions. That study found that the correlation was highest at quarterback (that study can be found here if you’re interested in the numbers behind the claim). That leaves us with our second major rule for this slate:
Limit Jimmy Garoppolo to 30 percent ownership and boost the Las Vegas kicker on rosters without Garoppolo. The two should theoretically never be played together.
Interestingly enough, the Lions have been even more concentrated than the Raiders this season, with Sam LaPorta and Amon-Ra St. Brown combining to account for 52.3 percent of the team’s total targets on the year. With David Montgomery out of action in Week 7, Jahmyr Gibbs handled a robust 87 percent snap rate and saw 21 running back opportunities, 10 of which were targets. In fact, St. Brown, LaPorta, and Gibbs combined to see 36 targets on 53 Jared Goff pass attempts their last time out, good for an elite 67.9 percent combined market share. Furthermore, the Raiders rank 30th in the league in red zone touchdown rate allowed at a robust 73.91 percent (as in, teams are scoring a touchdown on 73.91 percent of their red zone trips against the Raiders this season). Rule number three for MNF:
Jared Goff on 70 percent of rosters, paired with at least two of Amon-Ra St. Brown, Sam LaPorta, and Jahmyr Gibbs.
Finally, Tre Tucker has out-snapped Hunter Renfrow in each of the previous two games while Austin Hooper, Kalif Raymond, Antoine Green, Jameson Williams, and Brock Wright have all played around the same percentage of snaps during each team’s previous two games. Our final rule for the evening:
Exactly one player from the group of Tucker, Hooper, Raymond, Green, Williams, and Wright on every roster.
As you can see, the Contrarian Edge Optimizer is an invaluable tool to building bulk rosters for DFS play. To best harness its abilities, we must first have a working knowledge of the varying options to utilize as inputs in addition to a theoretical and conceptual working knowledge of the game of NFL DFS. This article series will attempt to further our understanding in both those areas throughout the season.
Contrarian Edge Optimizer can help your MNF lineups
An optimizer is a powerful tool for DFS, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. But we must first learn how to wield an optimizer to realize its true power.
That’s what we try to do in this weekly series. We’ll focus on Sportstopia’s Contrarian Edge Optimizer for every Monday Night Football game this season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the industry’s newest and most dynamic optimizer.
MNF: San Francisco 49ers at Minnesota Vikings
Running the Contrarian Edge Optimizer without altering any inputs returns a roster consisting of Alexander Mattison at captain, Brock Purdy, Brandon Aiyuk, George Kittle, T.J. Hockenson, and Brandon Powell. While I agree this returns the best combination of median projections, there are additional things we need to be thinking through when taking team tendencies, injuries, and game environment into account.
First off, Deebo Samuel is set to miss the next two games, at minimum, for the 49ers with a fracture in his shoulder. That takes an already concentrated San Francisco offense and turns it into a hyper-concentrated offense. Christian McCaffrey, Aiyuk, and Kittle should be at the top of our list on this showdown slate. That said, team tendencies could narrow that down further for us.
Vikings defensive coordinator Brian Flores has blitzed 22 percent more than any other team. Offensive tackle Trent Williams, one of the top tackles in the game, is listed as doubtful. When you combine those two truths, we’re left with a matchup against the most blitz-heavy defense in the league without one of the top pass protectors in the league for the 49ers.
Taking previous coaching tendencies into account, we should expect TE Kittle to play heavier rates in-line. That does not mean that he won’t run routes, but I would expect Kittle to be in to block at a higher rate than we’ve seen to this point in the season. Kittle’s 89.6 percent route participation rate could take a substantial hit in this spot.
That should place increased emphasis on Aiyuk through the air in addition to a high expected workload for McCaffrey. But it should also open up some secondary players for potential fantasy goodness, primarily Ray-Ray McCloud, who filled in directly for Samuel once the latter left the team’s Week 6 contest. That’s important as it wasn’t the more straight-up Jauan Jennings that saw an increase to his snap rate and route participation.
As for the Vikings, who will be without alpha wide receiver Justin Jefferson, things get a bit more interesting. Mattison projects well due to his hefty workload in this offense, but the 49ers force one of the highest pass rates against due to their suffocating run defense. That means K.J. Osborn, Jordan Addison, Powell, and Hockenson get a slight boost to expectations in this matchup.
Simply applying a 10 percent boost to the projections of Aiyuk, McCaffrey, McCloud, and the Vikings pass-catchers and applying a 10 percent decrease to Kittle, Mattison, and Jauan Jennings will force the optimizer to emphasize these theoretical findings. The biggest problem with only manipulating the skill position players is that it could preclude the optimizer from including defenses and kickers at a comparable rate as would otherwise be considered without manipulating projections.
To combat this, I recommend running the optimizer without manipulating the projections of those two positions for one-third of your entries, saving one-third for the base run and one-third for entries where you manipulate the defenses and kicker projections. This will give you the best mix of theory, its application, and variance management for a highly variant one-game sample.
Contrarian Edge Optimizer a great tool for DFS points
An optimizer is a powerful tool for DFS, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. But we must first learn how to wield an optimizer to realize its true power.
That’s what we try to do in this weekly series. We’ll focus on Sportstopia’s Contrarian Edge Optimizer for every Monday Night Football game this season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the industry’s newest and most dynamic optimizer.
MNF, Week 6: Dallas Cowboys at Los Angeles Chargers
The first run from the CEO, without altering any settings, returns a MNF showdown roster consisting of Michael Gallup at captain, Justin Herbert, Keenan Allen, Dak Prescott, Jake Ferguson, and CeeDee Lamb. Clearly, the optimizer likes the passing game from each team. We’ll cover some of the theoretical implications of a low-priced captain below.
There are two primary theoretical principles that guide captain selection in showdown – you either need the highest scoring player from a raw points perspective or an over-performing cheap player that unlocks the ability to gain exposure to numerous pay-up options throughout the remainder of the roster.
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In the first instance, separator scores are most valuable as no other player comes close to matching the production on a slate from one of the players. In the latter instance, and as is pertinent to the returns from the optimizer on first run, scoring is condensed at the top from a raw point total perspective, making it necessary to capture multiple players from the top tier of player pricing.
The hit rates are typically greater to capture the highest raw point total in the captain spot, but so too will the ownership be.
This then becomes an interesting discussion regarding hit rate, ownership, and leverage. From a theoretical sense, there are two paths to top-end scoring from a pass-catcher – we either need to capture bulk scoring through yardage (a downfield role) or touchdowns.
Either of those cases theoretically ties the pass-catcher to their respective quarterback, which the optimizer has done with Gallup and Ferguson present on the optimal roster. Even so, we must realize that touchdowns and deep shots downfield are two of the most variant acts found in the NFL, making a roster like this highly variant as well.
As such, optimal utilization of a roster like this would be directly tied to ownership. In other words, this roster is likely to be duplicated on this slate and would become less optimal due to the high variance included via the use of Gallup in the captain slot and another variant piece in Ferguson. It is typically a higher expected value stance to target variance at low ownership.
To continue that discussion, Gallup is currently projected for around 17 percent ownership in the captain slot due to what he opens up on the rest of the roster. As we just discussed, he is a highly volatile play on this slate while working in a downfield role on the Cowboys offense, which is typically a position to take at low ownership and relatively fade at higher ownership.
For comparison, Austin Ekeler is currently projected for around 6 percent ownership at captain yet offers clear paths to being the top overall scorer on the slate, with his low ownership likely influenced by the combination of uncertainty surrounding his first game back from injury and the state of the slate, with so many high-priced viable options that people want to jam into rosters.
A bet like Ekeler in the captain slot is a +EV bet to make over the long run, considering he is a guy that can return outlier scoring and be the highest scoring player in this game at a rate greater than his 6 percent ownership.
To harness this leverage angle, lock Ekeler into the captain slot and run the optimizer. See what types of rosters are returned. My personal favorite from the list of returned rosters with Ekeler at captain includes Justin Herbert, Dak Prescott, Ferguson, Cameron Dicker (the kicker), and Gallup.
As you can see, both variant players that were present on the initial run (Gallup and Ferguson) are still present on this roster but the combinatorial ownership of the roster as a whole is far less with Ekeler at captain, meaning we’re fighting with fewer rosters at the top (and potentially would be splitting first place with fewer rosters should it hit!).
This was an exercise in the marriage of theoretics and analytics, using our knowledge of game theory to influence the Contrarian Edge Optimizer’s process only slightly. There are clearly other theoretical angles to play on this slate, which you can use to build a +EV portfolio on a single-game slate.
'HIlow' helps you harness the power of our game-changing tool
An optimizer is a powerful tool for DFS, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. But we must first learn how to wield an optimizer to realize its true power.
That’s what we are trying to do in this weekly series. We’ll focus on Contrarian Edge Optimizer use at Fantasy Sports Logic for every Monday Night Football game this season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the newest and most dynamic optimizer in the industry.
Running the Contrarian Edge Optimizer without altering anything is always a good idea to see where the varying projections systems are leading rosters for the Packers-Raiders game on Monday night. Almost unanimously, you’ll see a high emphasis on the primary Raiders pieces (Davante Adams, Josh Jacobs, and Jimmy Garoppolo), followed by Packers Jordan Love and Christian Watson. That gives us a solid starting point to guide our discussion. Austin Hooper is also projecting well as the player with the lowest price who also carries a numerical projection on the slate.
The game between the Raiders and Packers pits one extremely concentrated offense with a middling defense (Las Vegas) against a relatively concentrated offense with a middling defense (Green Bay). As such, expect the kickers and defenses to go relatively under-owned with a high emphasis on the offensive pieces of each offense. And while that is the likeliest scenario in a spot like this, rosters that include either defense and/or one, or both, kickers are going to be solid leverage opportunities. In other words, the ownership on those pieces is likely going to be lower than the chances of them contributing to the optimal roster.
There are also numerous spots where ownership might come in lower than it otherwise would considering injury uncertainty, with Davante Adams, Aaron Jones, and Christian Watson either coming in questionable (Adams and Jones) or bringing uncertainty surrounding their expected snap rates as they work their way back from extended absences (Jones and Watson). Watson’s snap rate is likely to directly influence his projection and the projection of Romeo Doubs, and an “either or” stance is likely a good idea in this spot.
With so much uncertainty heading into the slate, it’s best to sort out your intended captains and run the optimizer with certain hard guidelines in place. For example, some of the rules that will guide those runs on this slate are:
One of the aspects of the optimizer that we haven’t discussed is the ability to edit the blend of the seven projections machines that are utilized by the Contrarian Edge Optimizer away from an even 14 percent split. On the top header, select “Edit Blend” and input the desired emphasis. While I don’t recommend altering these values on main slates, it can be invaluable for smaller slates and showdowns to leverage the varying projection systems from around the industry.
Feeling higher or lower on a specific player than the projections? Simply alter the max or min exposure thresholds for the optimizer, which will force varying builds away from the chalk. We can be as restricting as we want in this process, with the ability to restrict the optimizer down to a narrow band within two to three percent.
Check out our weekly Monday series on the Optimizer
An optimizer is a powerful tool to harness in DFS, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. But one must first learn how to wield an optimizer before its true power can be realized. That is what we will sort through in this weekly series.
We’ll focus on Contrarian Edge Optimizer use at Fantasy Sports Logic for each Monday Night Football to explore the tool, maximize expected value through optimal utilization and provide a sneak peek into the newest and most dynamic optimizer in the industry.
We’ll start our process for Giants-Seahawks in Week 4 by running the Contrarian Edge Optimizer without manipulating any functions. The optimizer returns a showdown roster of the Giants' Daniel Jones (captain) and Wan’Dale Robinson, and the Seahawks' D.K. Metcalf, Geno Smith, Tyler Lockett and Jaxon Smith-Njigba.
Then we explore underlying metrics and DFS Theory to guide the roster-building process. Seattle has historically held elevated rush rates in the green zone (within 10 yards of the end zone). Lead back Kenneth Walker now has nine such opportunities through three games, four of which he has converted to touchdowns.
Since touchdowns are extremely important to DFS output, and even more so in a Showdown format, capturing the touchdowns in a single-game format becomes increasingly important. In other words, Walker’s robust green zone role makes his chances of scoring a touchdown increasingly likely, which should boost our inputs into the algorithm.
VIDEO: Daryl Snyder and R.C. Fischer preview Seahawks-Giants. Find out which top players they are paying up for and who they're targeting as value plays. These two DFS experts will make a run at the DraftKings Millionaire Maker Tournament! (Story continues below the video)
On the other side, Seattle’s struggles in the red zone through three games should not be understated. The Seahawks are the only team in the league to allow a touchdown on every opponent red zone possession this season.
That bodes well for the touchdown expectation of a Giants team that has performed well in the red zone. Their 62.5 percent red zone TD rate ranks eighth. With Saquon Barkley listed as doubtful, the chances that those touchdowns flow through quarterback Daniel Jones are elite.
The books agree. Kenneth Walker and Daniel Jones carry the shortest odds to score a touchdown. This makes the inclusion of each player a solid starting point for the optimizer.
Touchdowns become even more important the lower we get in the salary totem pole. The optimizer loves Wan’Dale Robinson on this slate, and for good reason. He returned to the active roster in Week 3 for the first time since tearing his ACL in 2022. In that game, he played only 11 offensive snaps but saw five targets, good for a ridiculous 55.6 percent targets per route run rate.
Furthermore, his snaps came at the direct expense of Parris Campbell out of the slot. As such, any roster with Robinson should exclude Campbell in the event we see Robinson’s snap rate increase. Robinson is priced at just $3,000 and makes an interesting salary-saving option.
On the other side, Noah Fant and Will Dissly are questionable. Dissly had an improving trend throughout the week after missing Week 3 due to a shoulder injury, whereas Fant popped on the injury report for the first time Saturday, listed as a “DNP” with a knee injury.
Either way, an absence from either would correlate directly to increased snaps for Colby Parkinson, who has been the lead tight end. He holds a low 18.8 percent targets per route run rate, but he has 1.81 yards per route run (fifth), 9.7 yards per target (second), and 14.5 yards per reception (first). Any increase in snap rate will boost his projections in this spot. At only $800, he makes for an excellent salary-saving option in Showdown.
Boosting the projections of Jones, Walker, Robinson, and Parkinson returns a roster of those four plus Metcalf and Geno -- a solid roster but one that is likely to be duplicated immensely. Simple acts like substituting Tyler Lockett for Metcalf or moving either Metcalf or Lockett into the Captain spot uses less combined salary and are interesting ways to differentiate. Robinson is also priced around the kickers, which makes an easy pivot across various rosters.
Finally, Metcalf and Lockett historically struggle to provide ceiling games in unison, making a rule of “either Metcalf or Lockett” a solid bet in this spot.
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Learn the Contrarian Edge Optimizer's true power
An optimizer is a powerful tool to harness in DFS play, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. Like any other tool, one must first learn how to wield an optimizer before its true power can be realized.
That is exactly what we will look to sort through in this weekly series. We’ll focus on Contrarian Edge Optimizer use at Fantasy Sports Logic for the Monday slates each week of the 2023 season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the newest, and most dynamic, optimizer in the industry.
Most of the articles in this series will cover Showdown slates while we explore Monday Night Football slates. This week, however, we have a Monday Night Football doubleheader on the docket – Eagles at Buccaneers and Rams at Bengals – that will allow us to explore more of the full range of tools at our disposal through the Contrarian Edge Optimizer.
Open the Contrarian Edge Optimizer, select the slate for which you want to build, and run the optimizer. Don’t touch a single thing. Now analyze the rosters that are provided. The first roster that is generated through the blended methodologies for the Monday slate includes Matthew Stafford, Kyren Williams, Joe Mixon, Chris Godwin, Ja’Marr Chase, Mike Evans, Cade Otton, Puka Nacua, and the Bengals defense.
This practice will allow you to get a feel for the various projections from around the industry in addition to seeing how those pieces fit together optimally on a single roster.
In other words, simply running the optimizer without manipulating inputs gives insight into expected field tendencies, how salaries fit together on a roster, and the strongest median projection for a give slate.
Now, remove Joe Burrow from the player pool, apply a 20 percent decrease to Bengals skill position players, add Jake Browning to the player pool, and run it again. You can now visualize how the status of Joe Burrow might influence the median outcomes from the slate ahead.
You can even go as far as excluding the Bengals entirely, providing a boost to the Rams defense, and running it again. That returns a roster of Matthew Stafford, Kyren Williams, Rachaad White, Mike Evans, A.J. Brown, Puka Nacua, Cade Otton, Chris Godwin, and the Eagles defense. As you can see, the return is vastly different than the original roster returned.
There are certain aspects of profitable DFS play that professionals utilize at a higher rate than the field. The reasons for these practices have to do with historical hit rates versus utilization rate from the field.
A few examples of these practices include team over-stacks, QB-RB-TE correlations, and onslaught rosters (rosters with three to four players from one roster and zero or one from the opposing side).
These practices build inherent leverage in rosters as they historically hit at a rate higher than they are utilized by the field.
• Over 70 percent of the optimal rosters over the previous three seasons did not include a correlated bring-back.
• 22 percent of the optimal rosters over the previous three seasons included a QB-TE correlation.
• 18 percent of the optimal rosters over the previous three seasons included the primary stack’s running back.
And yet, the field is not utilizing these practices at those historical hit rates.
To influence the algorithm in those ways, select “Pro Options,” toggle “Auto Team Stacking Bonus” and “By Position,” bump “QB-TE,” “QB-WR,” and “QB-RB,” and generate lineups. You’ll see the algorithm now account for heavier correlation amongst quarterbacks, tight ends, wide receivers, and their running backs, which provides the leverage we are looking for through these DFS Theory methodologies.
The algorithm now returns Matthew Stafford, Kyren Williams, Puka Nacua, and Tyler Higbee rosters. As you can see, all the previously mentioned DFS Theory practices are being accounted for by the Contrarian Edge Optimizer.
Feeling higher or lower about a player than the field and want to overweight or exclude them from your pool? Simply select the “lock” button or “exclude” (red circle with line through) button next to a player’s name and run the optimizer again.
As an example, in the Sunday Night Football game, the Pittsburgh Steelers had only four healthy wide receivers and it was clear that Allen Robinson would be largely confined to slot snaps, leaving Calvin Austin to play most of the offensive snaps on the perimeter alongside George Pickens.
I locked Austin into every roster and ran the optimizer. Similarly, amidst poor efficiency and growing concerns of Jaylen Warren’s increased involvement, I excluded Najee Harris from my player pool.
As you can see, the Contrarian Edge Optimizer is an invaluable tool to building bulk rosters for DFS play. To best harness its abilities, we must first have a working knowledge of the varying options to utilize as inputs in addition to a theoretical and conceptual working knowledge of the game of NFL DFS. This article series will attempt to further our understanding in both those areas throughout the season.
Mark Garcia breaks down some helpful tips on how to unleash the full potential of the Contrarian Edge Optimizer ahead of tonight's MNF games.
An optimizer is a powerful tool to harness in DFS play, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. Like any other tool, one must first learn how to wield an optimizer before its true power can be realized.
That is exactly what we will look to sort through in this weekly series. We’ll focus on Contrarian Edge Optimizer use at Fantasy Sports Logic for the Monday slates each week of the 2023 season to explore the tool itself, maximize expected value through optimal utilization, and provide a sneak peek into the newest, and most dynamic, optimizer in the industry.
It is no secret in today’s DFS scene that stacking and correlation are optimal practices to boost profitability. But why is that the case? Stacking and correlation simultaneously reduce the number of variables that need to go right and maximize ceiling when those variables do go right. In other words, stacking and correlation provide paths to bulk scoring by leveraging team tendencies in various game environments.
Targeting specific game environments in NFL DFS is typically the most optimal approach to utilizing these processes. A competitive game environment that pushes past it’s game total can provide more offensive plays run from scrimmage (more opportunity for fantasy points to accrue), more touchdowns (the bulk scoring function), increased pass rates (more opportunity for points in a PPR setting), and influence a team’s play calling tendencies (more aggression).
The Team Stacking dropdown toggle in the Contrarian Edge Optimizer provides the ability to quickly influence the optimizer’s logic towards heavier rates of team stacks. Simply select the dropdown menu and choose the team you would like to boost stacking with. While a powerful tool in and of itself, Fantasy Sports Logic’s Contrarian Edge Optimizer provides more fluidity through a nuanced team boost approach as well.
Most of the articles in this series will cover Showdown slates as we’re exploring Monday Night Football slates. This week, however, we have a Monday Night Football doubleheader on the docket, allowing us to explore more of the full range of tools at our disposal through the Contrarian Edge Optimizer.
The Auto Team Stacking Bonus toggle allows a percentage boost to be applied to players on the team that the quarterback is selected from. You can then apply the desired percentage boost to influence the optimizer’s decision-making to tilt the logic towards team stacks. This allows for more fluidity in its logic when compared to other more rigid tools that would attempt to force team stacking per prescribed rules. You can then influence the team of the stack by using both functionalities in the optimizer.
There are also certain theoretical stacking practices that the optimizer is well-equipped to handle. Through a study performed on the Milly Maker tournament on DraftKings, where I examined the weekly optimal rosters and compared them to the winning rosters each slate, I found that 22.2 percent of the optimal rosters included a quarterback paired with his tight end.
While that makes sense considering tight end scoring is heavily correlated to touchdown production, and the quarterback would be the player on the other end of those touchdowns, the field’s utilization of this practice falls short of its hit rate. That fundamentally provides leverage on the field.
Furthermore, I also found that running backs were included on the optimal stack 18 percent of the time, yet the field still largely avoids playing a running back with a quarterback. But if a quarterback is succeeding, it makes it that much more likely that his running back also succeeds due to the higher likelihood of things like drives reaching the red zone, touchdown opportunities, and overall game environment.
Both underutilized tendencies can be influenced by the user in the Contrarian Edge Optimizer through the By Position Stacking bonus functionality. Under “Set Pro Options,” select the Auto Team Stacking Bonus toggle and the By Position toggle. Then, apply the desired boost to tight end and running back, which will influence the optimizer’s inputs under specific conditions.
As you can see, the Contrarian Edge Optimizer is an invaluable tool to building bulk rosters for DFS play. To best harness its abilities, we must first have a working knowledge of the varying options to utilize as inputs in addition to a theoretical and conceptual working knowledge of the game of NFL DFS. This article series will attempt to further our understanding in both those areas throughout the season.
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Mark "HiLowFF" Garcia gives a crash course on the Contrarian Edge Optimizer and how to use it to create you MNF Showdown lineups.
An optimizer is a powerful tool to harness in DFS play, capable of bulk operations in an instant that would otherwise take hours of manipulation to perform manually. Like any other tool, one must first learn how to wield an optimizer before its true power can be realized. That is exactly what we will look to sort through in this weekly series.
We’ll focus on the Contrarian Edge Optimizer to use for Monday Night Football this season to explore the tool itself, maximize expected value through optimal utilization and provide a sneak peek into the newest, and most dynamic optimizer in the industry.
Before manipulating any of the settings in the optimizer, I first like to export the projections via a comma-separated values document, which provides all the raw projections used by the optimizer for each player on the slate. Before continuing, it is important to understand what these values represent.
By definition, a median represents a projection whose final outcome would land above the projection and below the projection an equal 50 percent of the time. As such, the modeling in these algorithms can be back tested to find reliability indexes and tweak the algorithm to provide further accuracy.
The idea of median is difficult for the human brain to comprehend. We like things simple, direct and to the point – which is what median projections aim to provide. We must realize that these top-level values are a numerical representation of a range of outcomes for each player on a given slate.
This range of outcomes will be different shapes, sizes, and magnitudes for every player and becomes one of the better inputs to manipulate to alter the output from the optimizer. Give it a try! Run the optimizer without manipulating any of the median projections and see what it provides.
Then, manipulate just one player’s median to a 60 percent outcome (multiply the raw projection by six and divide by five) and run the optimizer again to see how that changes the output in roster form. Higher on a player on a given slate than the median projections are accounting for? Bump their value in the CSV within their range of outcomes and see how the optimizer responds! And best of all, the directions to complete this step in the process are readily available in the top-level of the optimizer design.
Predictive analytics utilizes statistical modeling methods to predict future outcomes. In other words, predictive analytics attempts to utilize machine learning algorithms to create predictive models. With the optimizer, the behind-the-scenes work has been done for us, but we can manipulate the outputs by changing inputs as previously discussed.
Variance in Median Projections
The best way to visualize a range of outcomes projection, assuming we are provided with a median projection, is to utilize a bell curve. This bell curve will be situated about the median with an array of potential outcomes. Most bell curves are symmetrical about the median, but some players carry an asymmetrical distribution of values within their broader range. These players, largely considered “low floor-high ceiling” plays, are some of the most difficult to predict and project due to their lopsided array.
Median projections must also account for ambiguity in certain situations. For example, there is significant ambiguity associated with the Buffalo Bills and the expected snap rates for players from the slot. Deonte Harty, Khalil Shakir, Trent Sherfield, and rookie tight end Dalton Kincaid could all see slot usage, but the optimizer must account for these wide ranges of potential outcomes and display it through median projections in numerical form. This introduces significant variance in those projections, something we can look to manipulate to harness in our favor.
In numerical models, these statistical anomalies are best represented through standard deviation – but we can do things to manipulate these players manually in the optimizer.
First, select “Set Pro Options” on the top left of your screen in the optimizer. Next, toggle “Auto Ownership Bonus” in the dropdown menu. This function is used to set an ownership threshold and bonus to encourage the inclusion of players that are less owned, leveraging the variance associated with median projections and ownership values.
This functionality will also help to harness the second major statistical input to the modeling – expected ownership. Since the game of football includes high rates of variance, ownership projections are a valuable input to leverage in the process. These ownership bonus thresholds can be manipulated to increase or decrease exposure to variance.
Let’s put these practices in action for Week 1 using the Contrarian Edge Optimizer. We’ll focus on the ambiguity with the expected slot snap rates from the Buffalo Bills, alter inputs, and see the outputs from those deviations. We won’t be able to see the full roster outputs for obvious reasons (the optimizer is a paid tool), but we should be able to conceptualize the effects of these manipulations.
I changed the projection of Deonte Harty to an 80 percent outcome, accounting for the potential for him to see a slot snap rate that is higher than his expectation, imported the new data into the optimizer and ran the simulation without adjusting any other values or manipulating any of the Pro Options.
Deonte Harty returned as the optimal Captain in that run, at 0.6 percent expected ownership. Doing the same for Dalton Kincaid returns him as the optimal Captain. The same can be done in the other direction to account for outcomes below median projection, which is useful for variant acts like injuries and matchup induced outliers.
The first installment in this series was broader and more conceptual than it will be in the future, but hopefully it helped to establish a foundational and working knowledge of the power of the optimizer and how to manipulate top-level statistical inputs. From here on out, we’ll get into the optimizer on a deeper level. Best of luck in Week 1 and we’ll see you at the top of the leaderboards!
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