NLA stats & predictions

Trends and Statistics

Analyzing trends and statistics can offer valuable insights into how tomorrow's matches might unfold:

• Average Goals per Match: The NLA has seen an average of around six goals per match this season, indicating a balanced offensive capability across teams.
• Penalty Success Rate: Teams with higher penalty success rates tend to perform better under pressure, making this statistic crucial for predictions.
• Fouls Committed: Teams with fewer fouls tend to maintain better control over the game pace, which could be advantageous in close matches.
• Catch Steal Ratio: A higher catch steal ratio often correlates with strong defensive strategies, potentially impacting game outcomes.
<|repo_name|>eriktollerud/decadal-surface-temperature-change<|file_sep|>/code/figures/figure_7.py # -*- coding: utf-8 -*- """ Created on Mon Jul 15 @author: Erik Tollerud """ import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker as mticker from mpl_toolkits.basemap import Basemap # Load data sens = np.load('data/figure_7_sensitivity.npz') lon = sens['lon'] lat = sens['lat'] sens_map = sens['sens_map'] # Get sensitivity map bounds sens_max = np.nanmax(sens_map) sens_min = np.nanmin(sens_map) # Create figure fig = plt.figure(figsize=(6*1.,5*1.), dpi=200) ax = fig.add_subplot(111) # Create map m = Basemap(llcrnrlon=-180,llcrnrlat=-60, urcrnrlon=180,urcrnrlat=80, projection='mill',resolution='c',area_thresh=10000.) m.drawcoastlines(linewidth=0.5) m.drawparallels(np.arange(-90.,90.,30.),labels=[1,0,0,0],color='k',dashes=[1,1]) m.drawmeridians(np.arange(0.,360.,60.),labels=[0,0,0,1],color='k',dashes=[1,1]) m.drawmapboundary(fill_color='#D6EAF8',zorder=0) # Plot data x,y = m(lon,lat) cs = m.contourf(x,y,sens_map,cmap=plt.cm.RdBu_r, levels=np.linspace(sens_min,sens_max,num=21), extend='both') # Add colorbar cb = plt.colorbar(cs,ticks=np.linspace(sens_min,sens_max,num=11),orientation='horizontal') cb.ax.set_xticklabels([r'${:.1f}$'.format(tick) for tick in cb.get_ticks()]) cb.ax.tick_params(labelsize=14) # Format plot plt.tight_layout() plt.savefig('figures/figure_7.png') plt.show()<|file_sep|># -*- coding: utf-8 -*- """ Created on Tue Jun 28 @author: Erik Tollerud """ import numpy as np def load_netcdf(filename): # Import libraries import netCDF4 # Open file f = netCDF4.Dataset(filename,'r') # Read variables t = f.variables['time'][:] # Convert time units from days since ... into years since ... # Get time units from file time_units_str = f.variables['time'].units # Get year from file time units string (assumes format 'days since YYYY-MM-DD HH:MM:SS') year_start_str = time_units_str.split(' ')[-1].split('-')[0] # Convert year string into float year_start_float = float(year_start_str) # Convert days since year_start into years since year_start_float t_years_since_year_start_float = t / (365.) # Set years since year_start_float equal to years since reference year (1850) t_years_since_reference_year_float = t_years_since_year_start_float + (year_start_float - reference_year) # Set reference year equal to year_start_float if not specified elsewhere if reference_year == None: reference_year = year_start_float # Update time variable with new values t[:] = t_years_since_reference_year_float # Read lat/lon variables (if they exist) # Check if lat/lon variables exist in file # Check if 'lat' variable exists in file if 'lat' in f.variables.keys(): lat = f.variables['lat'][:] # Check if 'lon' variable exists in file if 'lon' in f.variables.keys(): lon = f.variables['lon'][:] # Check if 'latitude' variable exists in file if 'latitude' in f.variables.keys(): lat = f.variables['latitude'][:] # Check if 'longitude' variable exists in file if 'longitude' in f.variables.keys(): lon = f.variables['longitude'][:] # If lat/lon variables don't exist... # Else assume lat/lon variables don't exist # If latitude array was not read... # Else assume latitude array was read # If longitude array was not read... # Else assume longitude array was read # If both latitude/longitude arrays were not read... # Else assume both latitude/longitude arrays were read # Set lat/lon arrays equal to numpy arrays of zeros with same shape as first data variable # Find name of first data variable in netCDF file data_var_names_list = list(f.variables.keys()) data_var_names_list.remove('time') first_data_var_name = data_var_names_list[0] # Read first data variable first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar = f.variables[first_data_var_name][:] # If first data variable is four-dimensional... # Else assume first data variable is four-dimensional # If first data variable is three-dimensional... # Else assume first data variable is three-dimensional # If first data variable is two-dimensional... # Else assume first data variable is two-dimensional # If first data variable is one-dimensional... # Else assume first data variable is one-dimensional # If first data variable is scalar... # Else assume first data variable is scalar ## Set latitude array equal to numpy array of zeros with same shape as scalar value lat=np.zeros(1,dtype=np.float32) ## Set longitude array equal to numpy array of zeros with same shape as scalar value lon=np.zeros(1,dtype=np.float32) ## Set latitude array equal to numpy array of zeros with same shape as one-dimensional value lat=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[0],dtype=np.float32) ## Set longitude array equal to numpy array of zeros with same shape as one-dimensional value lon=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[0],dtype=np.float32) ## Set latitude array equal to numpy array of zeros with same shape as two-dimensional value lat=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[1:],dtype=np.float32) ## Set longitude array equal to numpy array of zeros with same shape as two-dimensional value lon=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[1:],dtype=np.float32) ## Set latitude array equal to numpy array of zeros with same shape as three-dimensional value lat=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[2:],dtype=np.float32) ## Set longitude array equal to numpy array of zeros with same shape as three-dimensional value lon=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[2:],dtype=np.float32) ## Set latitude array equal to numpy array of zeros with same shape as four-dimensional value lat=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[3:],dtype=np.float32) ## Set longitude array equal to numpy array of zeros with same shape as four-dimensional value lon=np.zeros(first_data_var_array_4d_or_3d_or_2d_or_1d_or_scalar.shape[3:],dtype=np.float32) return t,lat,lon def get_gmt_date_string_from_days_since_reference_year(days_since_reference_year): # Convert days since reference year into years since reference year years_since_reference_year = days_since_reference_year / (365.) # Convert years since reference year into integer number of years since reference year integer_years_since_reference_year_int16 = int(years_since_reference_year) # Convert years since reference year into fractional number of years since reference year fractional_years_since_reference_year_int16=int((years_since_reference_year-int(years_since_reference_year))*100000000.) # Combine integer number of years since reference year + fractional number of years since reference year into single integer number integer_plus_fractional_years_since_reference_year_int64=int(integer_years_since_reference_year_int16*100000000+fractional_years_since_reference_year_int16) # Convert integer number + fractional number into string integer_plus_fractional_years_since_reference_year_str=str(integer_plus_fractional_years_since_reference_year_int64) # Pad integer+fractional string so that it contains at least nine digits if len(integer_plus_fractional_years_since_reference_year_str)<9: integer_plus_fractional_years_since_reference_year_str="".join(["0"]*(9-len(integer_plus_fractional_years_since_reference_year_str)))+integer_plus_fractional_years_since_reference_year_str # Get date string corresponding GMT date corresponding integer+fractional number (yyyy-mm-dd format) gmt_date_string=gmt_date_string_from_integer_plus_fractional_number(integer_plus_fractional_years_since_reference_year_str) return gmt_date_string def get_days_from_integer_plus_fractional_number(integer_plus_fractional_number): # Split integer+fractional number into integer part + fractional part ## Convert integer+fractional number into string integer_plus_fractional_number_string=str(integer_plus_fractional_number) ## Get length of integer+fractional number string integer_plus_fractional_number_string_length=len(integer_plus_fractional_number_string) ## Get length of fractional part string fractional_part_string_length=integer_plus_fractional_number_string_length-8 ## Extract integer part string integer_part_string=integer_plus_fractional_number_string[:8] ## Extract fractional part string fractional_part_string=integer_plus_fractional_number_string[-fractional_part_string_length:] ## Convert integer part string back into integer integer_part=int