New O and D Line Rankings

[highlyprofitable108]

Defensive Line Rating

For the defensive line, you want to capture their ability to disrupt the offense. Key stats could include:

• Sacks: Direct measure of the defensive line's ability to take down the quarterback.
• Tackles for Loss (Tloss): Shows how often the defensive line stops plays behind the line of scrimmage.
• Quarterback Hits (Qb Hits): Even if they don't result in sacks, they pressure the quarterback.
• Hurries: Forces the quarterback to throw before he's ready.
• Forced Fumbles: Indicates the defensive line's ability to create turnover opportunities.
• Knockdowns: Reflects the defensive line's ability to disrupt passing lanes.

You could create a rating by normalizing each of these statistics (e.g., converting them to a scale of 0 to 100) and then taking a weighted average based on their perceived importance.

Offensive Line Rating

For the offensive line, the focus is on protection and run blocking. Key stats could include:

• Pocket Time: Average time the quarterback has in the pocket before throwing.
• Sacks Allowed: Fewer sacks indicate better protection.
• Hurries Allowed: Fewer hurries indicate better overall pass protection.
• Knockdowns Allowed: Indicates the offensive line's ability to keep defenders away from the quarterback.
• Rushing Yards Before Contact: Reflects the offensive line's ability to open up lanes for the running back.
• Yards After Contact (YAC): While this is more reflective of the running back's ability, a good offensive line can contribute to higher YAC by creating initial openings.

Again, you would normalize these stats and then take a weighted average. It's important to consider that some stats may be inversely related to line performance (like sacks allowed), so they would need to be inverted (e.g., a lower number of sacks allowed would contribute to a higher rating).

Here's a simple example of how you might calculate these ratings:

def calculate_defensive_line_rating(sacks, tloss, qb_hits, hurries, forced_fumbles, knockdowns):
    # Normalize each stat to a 0-100 scale based on league max/min for the season
    # Then take a weighted average
    # Weights are hypothetical and should be determined by analysis
    weights = {'sacks': 0.25, 'tloss': 0.2, 'qb_hits': 0.2, 'hurries': 0.15, 'forced_fumbles': 0.1, 'knockdowns': 0.1}
    stats = {'sacks': sacks, 'tloss': tloss, 'qb_hits': qb_hits, 'hurries': hurries, 'forced_fumbles': forced_fumbles, 'knockdowns': knockdowns}

    rating = sum(weights[stat] * normalize(stats[stat]) for stat in stats)
    return rating

def calculate_offensive_line_rating(pocket_time, sacks_allowed, hurries_allowed, knockdowns_allowed, rushing_yards_before_contact, yac):
    # Normalize and invert where necessary (e.g., fewer sacks allowed is better)
    # Weights are hypothetical and should be determined by analysis
    weights = {'pocket_time': 0.2, 'sacks_allowed': 0.25, 'hurries_allowed': 0.25, 'knockdowns_allowed': 0.15, 'rushing_yards_before_contact': 0.1, 'yac': 0.05}
    stats = {'pocket_time': pocket_time, 'sacks_allowed': sacks_allowed, 'hurries_allowed': hurries_allowed, 'knockdowns_allowed': knockdowns_allowed, 'rushing_yards_before_contact': rushing_yards_before_contact, 'yac': yac}

    rating = sum(weights[stat] * (100 - normalize(stats[stat])) if stat in ['sacks_allowed', 'hurries_allowed', 'knockdowns_allowed'] else weights[stat] * normalize(stats[stat]) for stat in stats)
    return rating

def normalize(value, min_value=0, max_value=100):
    # Placeholder normalization function, should be replaced with actual normalization logic
    return (value - min_value) / (max_value - min_value) * 100

In this example, normalize is a placeholder function that you would need to replace with actual logic based on the range of values for each statistic. The weights are also hypothetical and should be determined based

[highlyprofitable108]

Implemented advanced.offensive and .defensize line metrics in dev and added to mongo. Testing...