Hammarby IF (w) vs Malmö FF (w)

Expert Betting Insights

Betting on Teams’ Historical Performance

The data shows that betting odds are heavily influenced by recent performances and historical results.

Expert Predictions for Each Team:

General Overview of Each Team:

• Hammarby’s team has shown significant improvement in recent matches, winning 5 out of the last 10 games played.
• Malmö’s defense is known for conceding few goals per game.

Hammarby IF (w)

Malmö FF (w)

Predictions:

Betting List Analysis: Football Matchup Odds and Predictions

The match between two formidable teams is highly anticipated. The data indicates several key trends and trends that could influence the outcome.

Betting List: Favoritism

• This match is expected to be tightly contested due to the recent performance of both teams.
  • Home Team Advantage: With home advantage, the home team has historically performed well in front of their own fans.
    • Odds are slightly in favor due to home-field advantage.

    Betting List – Defensive Prowess

    Analyzing from various angles:
    – Hammarby’s defensive strategy may lead to “home win”.
    – There is a strong possibility that both teams will be looking to exploit defensive lapses in each other’s play.

    In conclusion:

    The data indicates a high probability for both teams to score more than once in total. The historical performance metrics and current form support this view.
    

    Hammarby IF (w)

    WLWWW-

    Malmö FF (w)

    LWWWWDate: 2025-09-06Time: 11:00
    (FT)Venue: Tele2 ArenaScore: 2-1

    Predictions:

    Market	Prediction	Odd	Result
    Both Teams Not To Score In 1st Half	92.30%	(2-1)
    Over 1.5 Goals	78.20%	(2-1) 1.17
    Home Team To Score In 1st Half	75.20%	(2-1)
    Home Team To Score In 2nd Half	72.00%	(2-1)
    Over 2.5 Goals	75.10%	(2-1) 1.55
    Both Teams To Score	60.00%	(2-1) 1.75
    Away Team To Score In 2nd Half	62.40%	(2-1)
    Over 0.5 Goals HT	62.40%	(2-1) 2-0 1H 1.25
    Over 2.5 BTTS	59.00%	(2-1)
    Over 3.5 Goals	58.10%	(2-1) 2.38
    Home Team To Win	54.90%	(2-1) 1.44
    Both Teams Not To Score In 2nd Half	56.50%	(2-1)
    Avg. Total Goals	5.14%	(2-1)
    Avg. Goals Scored	3.04%	(2-1)
    Avg. Conceded Goals	1.60%	(2-1)

    Betting List: Match Outcome Projections

    The data suggests that with their offensive strategies, we should pay close attention to these aspects:
    – The likelihood of scoring over three times per half.

    The chances are that they might win if they keep playing with them.

    Betting List: Main Events:

    • Ahead of time:

    – Make sure you’re well prepared.

  • Hammerhead (winner) would be scored against you if they were not there at all.
  • Matched with appropriate preparation:

  ### Subsection: Expert Predictions

  #### Betting Insights

  Based on current form and historical performance metrics:

  Prediction Analysis:

  Predictions:
  – The most probable outcome for betting would be related to possible scores
  – Considering the average goals scored in recent matches as well as today’s conditions
  – Assuming there will be a lower chance of conceding goals in this match, then
  – Taking into account the potential scores from this game

  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt

  def create_dataset(series):
  X = []
  y = []
  for i in range(len(series)-1):
  X = []
  for j in range(12):
  X.append(i)
  X.append(y)
  return X,y

  def split_data(dataset,n_steps_in,n_features):
  n_samples,n_features = dataset.shape[0]
  n_in = n_steps_in
  n_out = n_steps_out
  train_size = int(len(dataset)*0.7)
  train,test = int(len(dataset)*0.8)
  test_size = len(dataset) – train_size

  #TODO Implement sliding window

  def fit_lstm(trainX,testX):
  model = Sequential()
  model.add(Dense(100)
  model.add(Dense(n_features))
  model.add(Dense(100)
  model.add(LSTM(128)
  model.add(Dense(50)
  model.compile(loss=’mae’, optimizer=’adam’)

  return [trainX[:train_size,:],testX[:test_size,:],y_train,y_test]

  # prepare input data
  def window_data(dataframe,n_steps_in=1,n_steps_out=1):
  X,Y = [],[]

  for i in range(len(dataframe)-n_steps_in):
  end_ix = i + n_steps
  seq_x = all_values[i:end_ix]
  X.append(dataframe.values[i:end_ix])
  seq_y.append(values[end_ix])
  y.append(values[end_ix])

  return X,y

  def main():

  [0]: ”’create sequence”’
  [1]: #create dataset from dataframe

  [2]: #load dataset

  [3]: ”’
  [4]: # load dataset

  [5]: # fix random seed for reproducibility

  [6]: ”’
  [7]: # load the training dataset from csv file into a pandas dataframe.

  [8]: ”’
  [9]: ”’
  [10]: df = pd.read_csv(“pollution.csv”)

  [11]: print(df.head())

  [12]: ”’
  [13]: ”’
  [14]:

  [15]: ”’
  [16]”’

  [16]”’Jooonghyun/ML-PYTHON/src/house-prices/train.py
  import numpy as np
  import pandas as pd
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import mean_squared_error
  from math import sqrt
  from sklearn.metrics import r2_score

  # Importing Libraries

  df_train = pd.read_csv(“train.csv”)
  # Check what columns we have

  ”’
  for col in df_train:
  print(df_train.columns)
  print(col)

  ”’
  **Loading Data**

  df_train = pd.read_csv(‘https://raw.githubusercontent.com/JooVeer/BigMartSalesPrediction/BigMartTrain.csv’)
  df.head()
  ”’
  **Data Description**
  The dataset contains 5 columns:
  * Item_Identifier: Uniquely identifies each product.
  * Item_Weight: Weight of product.
  * Item_Fat_Content: Whether or not product contains fat.
  * Item_Visibility: The percent of total display area available to retailers that is dedicated to a product.
  * Item_Type: General category assigned to product based on various criteria.
  * Item_MRP: Maximum retail price (list price)of product.
  * Outlet_Establishment_Year:The year when retailer was established.
  * Outlet_Size:The size of retailer store based on ground area covered.
  * Outlet_Location_Type:The type of city where retailer store is located.
  * Outlet_Type:The type of retailer based on kind of products sold.
  * Item_Outlet_Sales:The sales of product in particular store.

  **Data Dictionary**
  Column Name | Description | Data Type | Values | Missing Values
  — | — | — | — | —
  Item_Identifier | Unique ID (The numbers before _c) | Object | NA | No
  Item_Weight | Weight | Float | NA | Yes
  Item_Fat_Content | Whether or not product contains fat | Object | Low Fat,Regular,NIL | No
  Item_Visibility | Percent of total display area available to retailers that is dedicated to a product | Float | NA | No
  Item_Type | General category assigned to product based on various criteria | Object | NA | No
  Item_MRP | Maximum retail price (list price)of product | Float | NA | No
  Outlet_Establishment_Year | Year when retailer was established | Integer | NA | No
  Outlet_Size | Size of retailer store based on ground area covered | Object | Small, Medium, High | Yes
  Outlet_Location_Type | Type of city where retailer store is located | Object | Tier 1, Tier 2, Tier 3 | No
  Outlet_Type | Type of retailer based on kind of products sold | Object | Supermarket Type1,Supermarket Type2,Supermarket Type3,Super Hightech Supermarket | No
  Item_Outlet_Sales | Sales of product in particular store (Target Variable) | Float | NA | No

  ## **Exploratory Data Analysis**

  Let us first check if there are any missing values present in our dataset.

  {r}
  colSums(is.na(df))

  As we can see above, there are some missing values present under *Item_Weight* column.

  Now let us see how many unique values we have under each column.

  {r}
  sapply(df,class)

  We have different types of data present under each column like integer, float or object.

  Now let us find out how many unique values we have under each column.

  {r}
  sapply(df,function(x){length(unique(x))})

  We can observe that there are only two unique values under *Item_Fat_Content*, four unique values under *Outlet_Size*, three unique values under *Outlet_Location_Type*, four unique values under *Outlet_Type*. Let us check how many unique values do we have under *Item_Type* column.

  {r}
  print(length(unique(df$Item_Type)))

  We have eighteen unique values under *Item_Type*. Let us check which item belongs to which category.

  {r}
  print(table(df$Item_Type))

  Now let us check which item belongs to which fat content category.

  {r}
  print(table(df$Item_Fat_Content))

  As we can see above there are some duplicate categories like low fat and LF or regular and reg. Let us remove those duplicates from our dataset.

  {r}
  df$Item_Fat_Content <- ifelse(df$Item_Fat_Content == 'low fat','Low Fat',
  ifelse(df$Item_Fat_Content == 'LF','Low Fat',
  ifelse(df$Item_Fat_Content == 'reg','Regular',df$Item_Fat_Content)))

  print(table(df$Item_Fat_Content))

  Let us check how many missing values do we have under *Item_Weight* column.

  {r}
  print(sum(is.na(df$Item_Weight)))

  We can see there are some missing values present under *Item_Weight*. Let us find out how many missing values do we have for each item type.

  {r}
  print(table(is.na(df$Item_Weight),df$Item_Type))

  We can observe that there are more missing values present under food category than non-food category.

  Now let us find out mean weight for each item type.

  {r}
  mean_weight <- aggregate(Item_Weight ~ Item_Type,data=df,FUN=mean)
  colnames(mean_weight)[colnames(mean_weight)=='Item_Weight'] <- 'Mean_Weight'
  head(mean_weight)
  tail(mean_weight)

  Let us merge mean weight data with our original dataframe.

  {r}
  df <- merge(x=df,y=mean_weight,by='Item_Type',all.x=TRUE)
  head(df)
  tail(df)
  colnames(df)[colnames(df)=='Mean_Weight'] <- 'Mean_Item_Weight'
  head(df)
  tail(df)
  str(df)

  Now let us replace all missing values under *Item_Weight* with corresponding mean weight value according to item type.

  {r}
  df$Item_Weight <- ifelse(is.na(df$Item_Weight),df$Mean_Item_Weight,
  df$Item_Weight)
  sum(is.na(df$Item_Weight))
  str(df)

  We can see there are no missing values present now under *Item_Weight*. Let us remove *Mean_Item_Weight* column now from our dataset since it has no use now.

  {r}
  df <- df[,!(names(df) %in% c('Mean_Item_Weight'))]
  head(df)
  tail(df)
  str(df)
  summary(df)

  As we can see above most number of items belong to food category. Let us check what is the mean item visibility for each item type.

  {r}
  item_vis_mean <- aggregate(Item_Visibility ~ Item_Type,data=df,FUN=mean)
  colnames(item_vis_mean)[colnames(item_vis_mean)=='Item_Visibility'] 0,’Vis_Mean’]))
  print(max(item_vis_mean[item_vis_mean$Vis_Mean >0,’Vis_Mean’]))

  df$item_visibility_nw 0,’Vis_Mean’])),max(item_vis_mean[item_vis_mean$Vis_Mean >0,’Vis_Mean’]),df$Item_Visibility)

  item_visibility_nw <- aggregate(Item_Visibility ~ Item_Type,data=df,FUN=mean)

  colnames(item_visibility_nw)[colnames(item_visibility_nw)=='item_visibility_nw'] <- 'item_visibility_nw'

  item_visibility_nw$item_visibility_nw_max <- max(item_visibility_nw$item_visibility_nw)

  item_visibility_nw$item_visibility_nw_scaled <- item_visibility_nw$item_visibility_nw/item_visibility_nw_max

  str(item_visibility_nw)

  item_type_scale_factor <- merge(x=df,y=item_visibility_nw,by='Item_Type',all.x=TRUE)

  str(item_type_scale_factor)

  df$item_scaled_Visibility <- df$item_visibility_nw/item_type_scale_factor$item_visibility_nw_scaled

  df$item_scaled_Visibility <- ifelse((is.na((df$item_scaled_Visibility))),0,(df$item_scaled_Visibility))

  head(sort(unique(round(df$item_scaled_Visibility))))

  sum(is.na((df$item_scaled_Visibility)))

  hist(x=df$item_scaled_Visibility,breaks=20,col='blue',xlab='Scaled Visibility',
  main='Histogram for Scaled Visibility')

  boxplot(x=df$item_scaled_Visibility,col='green',main='Boxplot for Scaled Visibility')

  summary((df$item_scaled_Visibility))

  str((df$item_scaled_Visibility))

  quantile(x=(df$item_scaled_Visibility),probs=c(0,.25,.50,.75,.90,.95,.99,.999),na.rm=TRUE)

  table(round((df$item_scaled_Visibility),digits=0))

  boxplot(x=df[df$Outlet_Establishment_Year==1998,'item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  boxplot(x=df[df$Outlet_Establishment_Year==1999,'item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  boxplot(x=df[df$Outlet_Establishment_year==2002,'item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  boxplot(x=df[df$Outlet_Establishment_year==2007,'item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  table(round((df[df$Outlet_Establishment_year==1998,'item_scaled_Visibility']),digits=0))

  table(round((df[df$Outlet_Establishment_year==1999,'item_scaled_Visibility']),digits=0))

  table(round((df[df$Outlet_Establishment_year==2002,'item_scaled_Visibility']),digits=0))

  table(round((df[df$Outlet_Establishment_year==2007,'item_scaled_Visibility']),digits=0))

  quantile(x=(df[df['Outlet_Establishment_year']==1998,'item_scaled_Visibility']),probs=c(0,.25,.50,.75,.90,.95,.99,.999),na.rm=TRUE)

  quantile(x=(df[df['Outlet_Establishment_year']==1999,'item_scaled_Visibility']),probs=c(0,.25,.50,.75,.90,.95,.99,.999),na.rm=TRUE)

  quantile(x=(df[df['Outlet_Establishment_year']==2002,'item_scaled_Visibility']),probs=c(0,.25,.50,.75,.90,.95,.99,.999),na.rm=TRUE)

  quantile(x=(df[df['Outlet_Establishment_year']==2007,'item_scaled_Visibility']),probs=c(0,.25,.50,.75,.90,.95,.99,.999),na.rm=TRUE)

  boxplot(x=df[df['Outlet_Size']=='Medium','item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  boxplot(x=df[df['Outlet_Size']=='Small','item_scaled_Visibility'],col='green',main='Boxplot for Scaled Visibility')

  boxplot(x=df[df['Outlet_Size']=='High','item_scaled_Visibility'],col='green',main='