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Each league-specific model employs similar methodology. First, Oh determines the key parameters governing how much a team is going to score. In basketball, for example, those parameters include the probability of a two-point field goal attempt, the probability of making the two-point attempt, the probability of a three-point attempt, the probability of making that attempt, the probability of drawing a foul and going to the free-throw line, etc.
He determines each team's key parameters based on its expected active roster. In addition to excluding injured players, Oh makes sure to include players who may not directly accumulate stats (i.e. offensive linemen in football), but play vital roles in the team's success.
Step two involves determining the key, defensive-differential parameters. In all sports, strength of schedule can vary significantly team-by-team. The SportsLine simulator does not rely on the actual stats allowed, but instead calculates the plus/minus of what the defense allowed compared to what the opponent was averaging entering that matchup.
For example, say Denver was completing 67 percent of their passes entering a game vs. New England. In the game, Denver completed 60 percent of their passes. The New England defensive-differential parameter for that game would be minus-7 percent. We focus on differential to account for differences in schedule and the types of opponents each team has faced.
Here's another example: Two hockey goalies both have a save percentage of 93. But one goalie allowed 0.35 percent more goals to his opponents than they scored against other opponents, while the other held opponents 0.35 percent below their average, goal-scoring rate.
Same save percentage, but one is a Top 10 goalie, the other a Bottom 10 goalie.
Next, Oh runs a Monte Carlo simulation – a problem-solving technique used to approximate the probability of certain outcomes by running multiple trial runs using random variables. The "computerized" offenses play a game vs. the computerized defenses. How well each offense performs in the simulation is based on the parameters being adjusted by defensive-differentials. Oh repeats the game simulation thousands of times.
He then uses these simulated results to generate a projected final score with the probability of each team winning, each team covering, and the total score going Over or Under.
When Oh's projection varies dramatically from the Vegas line or total, SportsLine calls it an "A" pick. When the projection is moderately different and/or other factors reduce his confidence in the pick, it's a "B" pick. "C" picks occur when Oh's projection is close to the Vegas line, and he makes no pick at all when the simulation and the line run too close together.
Lines can change in response to various, market factors. SportsLine update its simulations and picks on a regular basis, but they are not necessarily updated every time the consensus line changes. The SportsLine picks will not only include the team or side, but also the betting line on which the pick is based. As some sportsbooks offer lines that differ from the consensus, the Projected Point Spread would indicate how SportsLine's pick would compare to the line at that particular sportsbook. For example, if the consensus line is Dallas -2.5 over Philadelphia, and the SportsLine simulation projects Dallas to win by 3, the SportsLine consensus ATS pick would be Dallas: however, if a different sportsbook has a line of Dallas -3.5, then the pick for that sportsbook would be Philadelphia +3.5.
For MLB and NHL, the model provides a spread value pick. In these sports, the spread is always -1.5 for the money-line favorite and +1.5 for the money-line underdog; it never changes to a different value. It is very important for the "juice" to be factored in when determining SPREAD VALUE. The "juice" reflects how often the sportsbook expects either team to win, with a certain percentage added for the sportsbook's profit.
When the SportsLine model determines which team is the better SPREAD VALUE, it compares the percentage of times that the team covered the spread in the simulations versus the percentage of time the sportsbooks expected that team to cover – as reflected via the juice.
For example, Ottawa is a -145 favorite at home versus Florida (+125). The spread is Ottawa -1.5 at +185 and Florida +1.5 at -230. The Ottawa +185 and Florida -230 translate to the sportsbook expecting Ottawa to cover 33% of the time, and Florida 67%. In the SportsLine simulations, Ottawa covered the -1.5 goal spread 40% of the time, seven points higher than the 33% indicated by the juice. Ottawa -1.5 at +185 would be the SPREAD VALUE pick.
The SportsLine money line pick records and ROI are based on a 100 unit assumption. If Vancouver is a +114 underdog (45.6% Vegas win) to Detroit (-126, 54.4%), and Vancouver wins 49.3% of simulations, SportsLine would have a pick of VAN +114. If Vancouver wins, SportsLine would score the pick as being correct with a +114 profit off the 100 unit risk. If the pick was for Detroit at -126 and Detroit won, SportsLine would score the profit as +79 units off the 100 unit risk.
Picks are updated throughout the day in order to account for changes in the money line, provided by our partners at Covers.com. The final pick may not reflect the exact same line for any one particular sportsbook, or even in the most recent Covers update. In order to account for differences among books, we provide each team's projected winning percentage, which can be compared to the Vegas win percentage based on the lines at that particular book.
Picks are updated regularly – taking into account the changing lines across sportsbooks. If a particular sportsbook differs from the consensus, the projected simulation line compared to the line at that sportsbook would indicate what the pick would be.
For our NHL pick records, we do account for the over line and under line. These lines are often skewed significantly from the standard 10% "juice" (-110) that sportsbooks typically charge to win +100. For other leagues, we assume a -110 line for picking the over or the under.
A PICK: Strongest pick.
B PICK: Moderate pick. The model still likes one side, but various factors reduce its confidence.
C PICK: Slight lean.
In some cases, there may be no betting line (most common for college basketball over/under); in other cases, the SportsLine simulation will produce the same line as the betting line. In these cases, SportsLine will assign NO PICK.
The biggest single factor in determining better grades (A picks) is the difference between the SportsLine simulation-based line and the betting line. Larger differences equal higher grades. But there can be a variety of other factors that impact grades:
Small Sample Size: Whether due to injuries, player inexperience and/or major roster turnover, there are cases where the key offensive and defensive parameters from past games are not a good predictor of how the currently, comprised team will perform. When past stats are not relevant to predicting future events, we lower the pick grade.
Profitable Situation: In addition to large deviations between our line and the betting line, there may be cases where a smaller deviation is worth a high grade because it is a "profitable situation." For example, there may be a Money Line pick in MLB where there is a 5% difference in our simulation win % and the Vegas Win % for a team that is a +110 underdog. This might be an "A" pick. There may be another pick where we have a team favored by 70% and it is -180 favorites (62.7% Vegas Win %). Even though we have a larger 7.3% difference with this pick, SportsLine may only have this rated as a "B" pick because you only make +55 on a 100 unit risk.
Human Expertise: Sometimes the stats just cannot capture everything. There are cases where our "human experts" identify non-statistical reasons for increasing or decreasing the grade.
Lines move frequently as more information about a game becomes available and as money moves to one side or another. SportsLine simulations and picks are updated frequently to reflect how projected simulation results correlate with the current lines.
For the MLB and NHL, we provide the Money Line Win-Loss record, which is the same as the real Win-Loss record; for the NHL, there is no distinction for OTLs. We also provide the profit and loss against the Money Line (which often differs significantly from the standard win-loss record) as teams that lose frequently can occasionally pull upsets or can show a betting profit even with a losing record – which would otherwise be undervalued by the betting markets. Additionally, we provide Over-Under betting trends for all leagues.
We also tailor the trends to show the relevant splits. If a team is a road underdog, we show how that team has performed as a road underdog in the past. If a team is playing a defense that allows Under 96 points, we show how that team has performed against similarly tough defenses in the past. We also show relevant time frames – current season, since last season (includes current and previous season) and the past month (31 days actually). In many cases, especially in leagues with fewer games like the NFL, trends and data going back to the previous season are considered when setting lines early in each season.
In college sports, we only include match-ups versus other teams within their division. In CFB, we do not include non-FBS games; in CBB, we do not include non-Div 1 games.
The Projected Box Score has two other tabs: 1) they show projected points and salaries for key Daily Fantasy game providers and 2) provide information about which players are likely to be out of the lineup due to injury or other reasons.
Determining active lineups based on available data gives a high degree of probability, but falls short of a guarantee. The closer to game time will increase the likelihood of active lineups, but will yield limited time to research picks thoroughly. Our commitment will be to update our simulations regularly, based on the aforementioned content.
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