### Install kickscore via pip

Source: https://github.com/lucasmaystre/kickscore/blob/master/README.rst

This command installs the latest release of the kickscore library directly from the Python Package Index (PyPI). It is the standard way to get started with the library.

```Shell
pip install kickscore
```

--------------------------------

### Installing kickscore library (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Installs the kickscore library using pip, typically required when running in environments like Google Colaboratory.

```Python
!pip install kickscore
```

--------------------------------

### Install kickscore Library (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Installs the kickscore library using pip. This is typically needed when running in environments like Google Colaboratory.

```python
!pip install kickscore
```

--------------------------------

### Initializing a BinaryModel (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Creates an instance of the BinaryModel from the kickscore library, suitable for modeling outcomes where one item wins over another.

```Python
model = ks.BinaryModel()
```

--------------------------------

### Importing kickscore and setting up plotting (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Imports the kickscore library under the alias 'ks' and enables inline plotting for visualization in a notebook environment.

```Python
import kickscore as ks
%matplotlib inline
```

--------------------------------

### Fitting the kickscore model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Trains the kickscore model using the added observations to infer the latent skill parameters over time.

```Python
model.fit(verbose=True)
```

--------------------------------

### Define kickscore Model and Kernel (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Initializes a kickscore.BinaryModel suitable for pairwise comparisons and defines a composite kernel using Constant and Matern32 components. The kernel parameters are set based on prior optimization results, with lengthscale converted to seconds.

```python
# It is a bit more convenient to specify lengthscales in yearly units.
seconds_in_year = 365.25 * 24 * 60 * 60

model = ks.BinaryModel()
kernel = (ks.kernel.Constant(var=0.03)
        + ks.kernel.Matern32(var=0.138, lscale=1.753*seconds_in_year))
```

--------------------------------

### Defining item kernels and adding items to the model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Defines kernel functions to describe the temporal dynamics of item skills and adds items (contestants) to the model with their respective kernels.

```Python
# Spike's skill does not change over time.
k_spike = ks.kernel.Constant(var=0.5)

# Tom's skill changes over time, with "jagged" (non-smooth) dynamics.
k_tom = ks.kernel.Exponential(var=1.0, lscale=1.0)

# Jerry's skill has a constant offset and smooth dynamics.
k_jerry = ks.kernel.Constant(var=1.0) + ks.kernel.Matern52(var=0.5, lscale=1.0)

# Now we are ready to add the items in the model.
model.add_item("Spike", kernel=k_spike)
model.add_item("Tom", kernel=k_tom)
model.add_item("Jerry", kernel=k_jerry)
```

--------------------------------

### Fit the kickscore Model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Executes the model fitting process using the model.fit() method. This command is timed using %%time in the notebook environment. It prints a message indicating if the model fitting converged successfully.

```python
%%time
converged = model.fit()
if converged:
    print("Model has converged.")
```

--------------------------------

### Download NBA Data (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Downloads the NBA Elo dataset from FiveThirtyEight's website using the curl command executed from the Python environment and saves it as nba_elo.csv.

```python
!curl https://projects.fivethirtyeight.com/nba-model/nba_elo.csv -o nba_elo.csv
```

--------------------------------

### Import Required Libraries (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Imports necessary Python libraries: csv for data parsing, kickscore as ks for the modeling, datetime for time handling, and enables inline matplotlib plotting.

```python
import csv
import kickscore as ks

from datetime import datetime

%matplotlib inline
```

--------------------------------

### Plotting item skills over time (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Visualizes the inferred skill trajectories for specified items over time, showing the mean skill and one standard deviation confidence interval.

```Python
model.plot_scores(["Tom", "Jerry", "Spike"], figsize=(14, 5));
```

--------------------------------

### Calculate Future Probability with Model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Calculates the probability of one entity beating another at a future time point (t=2.0) using the model's `probabilities` method and prints the result.

```python
p_win, p_los = model.probabilities(["Jerry"], ["Tom"], t=2.0)
print("Chances that Jerry beats Tom at t = 2.0: {:.1f}%".format(100*p_win))
```

--------------------------------

### Calculate Past Probability with Model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Calculates the probability of one entity beating another at a past time point (t=-1.0) using the model's `probabilities` method and prints the result.

```python
p_win, p_los = model.probabilities(["Jerry"], ["Tom"], t=-1.0)
print("Chances that Jerry beats Tom at t = -1.0: {:.1f}%".format(100*p_win))
```

--------------------------------

### Parse NBA Data and Create Observations (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Reads the downloaded nba_elo.csv file, extracts team names, and creates a list of game observations up to a specified cutoff date (June 1, 2019). Each observation records the winner(s), loser(s), and timestamp of a game.

```python
teams = set()
observations = list()

cutoff = datetime(2019, 6, 1).timestamp()

with open("nba_elo.csv") as f:
    for row in csv.DictReader(f):
        t = datetime.strptime(row["date"], "%Y-%m-%d").timestamp()
        if t > cutoff:
            break
        teams.add(row["team1"])
        teams.add(row["team2"])
        if int(row["score1"]) > int(row["score2"]):
            observations.append({
                "winners": [row["team1"]],
                "losers": [row["team2"]],
                "t": t,
            })
        else:
            observations.append({
                "winners": [row["team2"]],
                "losers": [row["team1"]],
                "t": t,
            })

```

--------------------------------

### Adding observations to the model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Adds chronological observations of outcomes (winners and losers) at specific time points to the kickscore model for training.

```Python
# At first, Jerry beats Tom a couple of times.
model.observe(winners=["Jerry"], losers=["Tom"], t=0.0)
model.observe(winners=["Jerry"], losers=["Tom"], t=0.9)

# Then, Tom beats Spike, and then Jerry.
model.observe(winners=["Tom"], losers=["Spike"], t=1.7)
model.observe(winners=["Tom"], losers=["Jerry"], t=2.1)

# Finally, Jerry beats Tom, and then Tom + Spike.
model.observe(winners=["Jerry"], losers=["Tom"], t=3.0)
model.observe(winners=["Jerry"], losers=["Tom", "Spike"], t=3.5)
```

--------------------------------

### Access Model Log-Likelihood (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Accesses the log-marginal likelihood of the data given the model. This value can be used to compare different models; a higher value indicates a better fit.

```python
model.log_likelihood
```

--------------------------------

### Predicting outcome probabilities (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb

Uses the fitted model to predict the probability of a specific outcome (one item winning over another) at a given future time point.

```Python
# We can predict a future outcome...
p_win, p_los = model.probabilities(["Jerry"], ["Tom"], t=4.0)
print("Chances that Jerry beats Tom at t = 4.0: {:.1f}%".format(100*p_win))
```

--------------------------------

### Add Teams and Observations to Model (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Iterates through the collected set of unique team names and adds each team to the kickscore model with the defined kernel. Then, it iterates through the list of game observations and adds each game result to the model.

```python
for team in teams:
    model.add_item(team, kernel=kernel)

for obs in observations:
    model.observe(**obs)
```

--------------------------------

### Plot Team Skill Evolution (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Generates a plot showing the estimated skill evolution over time for specific NBA teams (Lakers, Bulls, Celtics) using the fitted model. The plot resolution and size are configured, and timestamps are used on the x-axis. A title is added to the plot.

```python
fig, ax = model.plot_scores(
        items=["LAL", "CHI", "BOS"],
        resolution=10/seconds_in_year,
        figsize=(14.0, 3.0),
        timestamps=True)
ax.set_title("Evolution of skill of NBA teams (1946–2019)");
```

--------------------------------

### Compute Outcome Probabilities (Python)

Source: https://github.com/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb

Uses the fitted model to calculate and print the probability of the Chicago Bulls beating the Boston Celtics at three different points in time (1996, 2001, and 2020).

```python
print("Probability that CHI beats BOS...")

p_win, _ = model.probabilities(["CHI"], ["BOS"], t=datetime(1996, 1, 1).timestamp())
print("  ... in 1996: {:.2f}%".format(100 * p_win))

p_win, _ = model.probabilities(["CHI"], ["BOS"], t=datetime(2001, 1, 1).timestamp())
print("  ... in 2001: {:.2f}%".format(100 * p_win))

p_win, _ = model.probabilities(["CHI"], ["BOS"], t=datetime(2020, 1, 1).timestamp())
print("  ... in 2020: {:.2f}%".format(100 * p_win))
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.