Schuylkill Catfish Odds Calculator

How This Works

This tool estimates the odds of catching a catfish on the Schuylkill River in Philadelphia by analyzing multiple environmental variables: barometric stability (D), recent pressure change (P), water level (L), and temperature (T).

Variables & Gauges

D
(Stable Days)

P
(Pressure Change)

L
(Gauge Height)

T
(Temperature)

Formula

Score = 0.30×D + 0.25×P + 0.20×L + 0.25×T
        

Each component is normalized to [0, 1] and weighted accordingly.

Mathematical Model

Here are the exact normalization formulas and full composite score. Variables:

Let:
  - p_now = current barometric pressure (in hPa)
  - p_prev = barometric pressure approximately 1 hour prior (in hPa)
  - T_now = current air temperature (°F)
  - L_now = current gauge height (e.g., river level in ft)
  - D_days = count of “stable days” over the past 4 days, defined by barometric averages

Normalization constants:
  - P_max = 10 hPa   (max pressure change magnitude assumed relevant)
  - T_opt = 75 °F
  - T_range = 15 °F  (temperature deviation beyond this yields zero)
  - L_opt = 7 ft
  - L_range = 5 ft   (level deviation beyond this yields zero)
  - D_max_days = 4   (we consider up to 4 days of stability)

Compute normalized components:
  1) Barometric stability, D:
     For each of the past 4 days, compute daily average pressure and compare successive days.
     Let stable_count = number of days (out of 4) where |avg_pressure_day_i - avg_pressure_day_{i-1}| ≤ 2 hPa.
     Then:
       D = min(stable_count, D_max_days) / D_max_days
     So D ∈ [0,1].

  2) Pressure change, P:
     Δp = |p_now - p_prev|
     P_raw = 1 - (Δp / P_max)
     P = clamp(P_raw, 0, 1)
     i.e. P = max(0, 1 - |p_now - p_prev| / 10).

  3) Gauge height, L:
     ΔL = |L_now - L_opt|
     L_raw = 1 - (ΔL / L_range)
     L = clamp(L_raw, 0, 1)
     i.e. L = max(0, 1 - |L_now - 7| / 5).

  4) Temperature, T:
     ΔT = |T_now - T_opt|
     T_raw = 1 - (ΔT / T_range)
     T = clamp(T_raw, 0, 1)
     i.e. T = max(0, 1 - |T_now - 75| / 15).

Composite score S (in [0,1]):
  S = (w_D * D) + (w_P * P) + (w_L * L) + (w_T * T)
Where weights:
  w_D = 0.30, w_P = 0.25, w_L = 0.20, w_T = 0.25.

Odds percentage:
  Odds% = 100 × S.

Thus in one expression:
  Odds% = 100 × (0.30·D + 0.25·P + 0.20·L + 0.25·T),
with D, P, L, T defined as above.

These piecewise functions serve to normalize the raw data into a value between [0,1]. Doing so shows how far each variable deviates from the 'optimal' range (given the location).